A method and system for detecting magnesium oxide adhesion of silicon steel

Abstract

The present application relates to the field of multi-sensor fusion technology, and in particular to a detection method and system for the adhesion of magnesium oxide to silicon steel. The present application performs a particle matching analysis based on the corresponding magnesium oxide coating particle conditions and the silicon steel surface conditions, evaluates the particle bonding effect based on the particle matching analysis results and the fluidity of the coating in the scene, and predicts the magnesium oxide adhesion quality based on the evaluation results of the particle bonding effect and the vibration of the equipment during the coating process. The coating characteristics, silicon steel surface conditions, coating environment and dynamic factors in the coating process are comprehensively considered to evaluate and analyze the magnesium oxide adhesion from multiple dimensions. This multi-dimensional analysis method provides a more scientific and accurate evaluation method for silicon steel coating technology.

Description

Technical Field

[0001] The present application relates to the field of multi-sensor fusion technology, and in particular to a method and system for detecting magnesium oxide adhesion of silicon steel. Background Art

[0002] As an important soft magnetic alloy, silicon steel has an irreplaceable position in many electrical fields such as electricity, electronics, and home appliances. It is widely used in the manufacture of iron cores for core electrical equipment such as transformers, motors, and generators. This is because silicon steel has low hysteresis loss and high magnetic permeability, which can effectively improve the energy conversion efficiency of electrical equipment and reduce energy consumption.

[0003] During the production and application of silicon steel, in order to further improve its performance and service life, a layer of magnesium oxide coating is usually applied to the surface of the silicon steel. The magnesium oxide coating has many important functions. It can be used as a high-temperature annealing isolation agent to prevent the silicon steel sheets from sticking to each other during the high-temperature annealing process, thereby ensuring the independence and integrity of the silicon steel sheets. At the same time, the magnesium oxide coating can also improve the surface quality of silicon steel, improve the corrosion resistance of silicon steel, and reduce the performance degradation of silicon steel caused by oxidation, corrosion and other factors during storage and use. In addition, the magnesium oxide coating also has a certain effect on the magnetic properties of silicon steel. A suitable magnesium oxide coating can optimize the magnetic domain structure of silicon steel and improve the magnetic properties of silicon steel.

[0004] Current methods for testing the adhesion of magnesium oxide to silicon steel suffer from numerous shortcomings. First, most existing methods focus solely on one or a few characteristics of the magnesium oxide coating, such as coating thickness and hardness, while ignoring numerous other factors that significantly influence adhesion. For example, in actual production, the particle characteristics of the magnesium oxide coating (such as particle size and uniformity), the microscopic condition of the silicon steel surface (such as surface pitting and flatness), the fluidity of the coating, and environmental factors during the coating process (such as vibration of the silicon steel during coating) all significantly affect the adhesion quality of magnesium oxide. However, existing testing methods fail to fully account for these factors, resulting in an inability to accurately and comprehensively assess magnesium oxide adhesion. Second, existing testing methods primarily focus on testing the magnesium oxide coating after application, lacking effective analysis and monitoring of the dynamic changes and influencing factors during the coating process. The coating process is a complex physical process, in which factors such as the flow of the coating and the vibration of the silicon steel will affect the adhesion quality of magnesium oxide in real time during the coating process. If the inspection is only carried out after the coating is completed, once the adhesion quality is found to be unqualified, it is difficult to trace and determine the specific cause of the problem, and it is impossible to adjust and optimize the coating process in time, resulting in low production efficiency and increased production costs. Therefore, a method and system for detecting the adhesion of magnesium oxide to silicon steel is urgently needed. Summary of the Invention

[0005] In order to overcome the defects and shortcomings of the prior art, the present application provides a method and system for detecting the adhesion of magnesium oxide to silicon steel.

[0006] In order to achieve the above objectives, this application adopts the following technical solutions:

[0007] In a first aspect, the present application provides a method for detecting the adhesion of magnesium oxide to silicon steel, comprising the following steps:

[0008] Step 1: Use the visual module to obtain the corresponding magnesium oxide coating particle situation and silicon steel surface situation, and at the same time obtain the fluidity of the coating in the scene and the vibration of the silicon steel during the coating process;

[0009] Step 2: Perform particle matching analysis based on the corresponding magnesium oxide coating particle situation and silicon steel surface situation;

[0010] Step 3: Evaluate the particle bonding effect based on the particle matching analysis results and the fluidity of the coating in the scene;

[0011] Step 4: Predict the adhesion quality of magnesium oxide based on the evaluation results of the particle bonding effect and the vibration of the equipment during the coating process;

[0012] Step 5: Provide an early warning of the adhesion effect based on the predicted result of the magnesium oxide adhesion quality.

[0013] In one implementation of the present application, the magnesium oxide coating particle condition includes the size of the magnesium oxide particles in the coating and the viscosity of the liquid in the coating, wherein the size of the magnesium oxide particles and the viscosity of the liquid in the coating are obtained through experiments, the silicon steel surface condition includes the size and shape data of the pits on the silicon steel surface, the fluidity of the coating in the scene is obtained through coating experiments, and the jitter condition of the silicon steel during the coating process is obtained by a jitter sensor to obtain the jitter condition of the silicon steel surface.

[0014] In one implementation of the present application, the particle matching analysis in step 2 includes the following specific steps:

[0015] S21. Obtain the size of the magnesium oxide particles and import the size of the magnesium oxide particles into a size uniformity analysis formula for size uniformity analysis. The size here can be expressed by particle size or volume. The size uniformity analysis formula is: , where n is the number of particles, xi is the particle size of the i-th magnesium oxide particle, and xp is the average particle size of the magnesium oxide particles. The size uniformity analysis formula is essentially a measure of the degree of dispersion of the particle size of each particle relative to the average particle size;

[0016] S22. Obtain the size and shape data of the etch pits on the silicon steel surface, and import the size of the etch pits on the silicon steel surface into the etch pit uniformity analysis formula to perform etch pit uniformity analysis, wherein the etch pit uniformity analysis formula is: , where m is the number of pits on the silicon steel surface, sj is the size of the i-th pit on the silicon steel surface, and sp is the average size of the pits on the silicon steel surface;

[0017] S23. Based on the results of the pit uniformity analysis and the size uniformity analysis, and the similarity in shape between the magnesium oxide particles and the pits on the silicon steel surface, a particle matching analysis is performed. The particle matching analysis formula is: , where Vmin() is the minimum volume of the image in brackets, Vmax() is the maximum volume of the image in brackets, as is the image shape of the standard magnesium oxide particles, and cs is the image shape of the standard etch pit. is the intersection of two images, is the union of two images, It represents the maximum value of the intersection of the images divided by the minimum value of the union of the images, which represents the similarity of the two images. PC is the result of size uniformity analysis, and PS is the result of pit uniformity analysis. The sum of the two represents the influence of the size uniformity of the pits on the surface of magnesium oxide particles and silicon steel on the matching degree.

[0018] In one implementation of the present application, the evaluation of the particle bonding effect in step 3 includes the following specific steps:

[0019] S31. Obtain the fluidity and viscosity of the coating in the scenario, and simultaneously obtain the pitting conditions on the silicon steel surface. Perform a surface flatness analysis of the silicon steel based on the pitting conditions to quantify the effect of the silicon steel surface flatness on the fluidity of the coating. Perform a coating flow anomaly analysis based on the silicon steel surface flatness analysis results and the fluidity of the coating in the scenario. The fluidity of the coating in the scenario is an indicator of the fluidity of the coating under temperature and humidity. The coating flow anomaly analysis formula is: , where Pz is the surface flatness of the silicon steel, Lz is the fluidity of the coating in the scene, which can be expressed by the flow velocity, and Lzm is the standard value of fluidity. The influence coefficient of silicon steel surface flatness on fluidity is obtained by obtaining the corrosion pits on the silicon steel surface and performing surface flatness analysis, which can quantitatively describe the flatness of the silicon steel surface. This helps to accurately understand the microscopic morphology of the silicon steel surface and provides a basis for subsequent research on the flow behavior of the coating on its surface.

[0020] S32. Obtain the coating flow anomaly analysis results, the coating viscosity, and the particle matching analysis results to evaluate the particle bonding effect. The particle bonding effect evaluation formula is: ,in, is the viscosity of the coating, For the standard viscosity case, is the influence coefficient of coating flow abnormality, The viscosity of the coating is the influence coefficient. The results of the coating flow anomaly analysis, the coating viscosity and the particle matching analysis are combined to evaluate the particle bonding effect, which can comprehensively consider various factors affecting the particle bonding effect.

[0021] In one implementation of the present application, the prediction of the magnesium oxide adhesion quality in step 4 includes the following specific contents:

[0022] S41. Obtain the vibration of the silicon steel during the coating process. Based on the vibration of the silicon steel during the coating process, perform a vibration impact analysis on the adhesion of magnesium oxide based on the vibration amplitude and vibration frequency of the silicon steel surface. The vibration impact analysis formula is: , where G is the average number of jitters during the monitoring period, Hg is the amplitude of the g-th jitter, and Hm is the safe jitter amplitude. The jitter condition of silicon steel during the coating process is obtained, and the impact of jitter on magnesium oxide adhesion is analyzed based on the jitter amplitude and jitter frequency. This can accurately quantify the impact of jitter, a dynamic factor, on magnesium oxide adhesion.

[0023] S42. Predicting the adhesion quality of magnesium oxide based on the particle bonding effect evaluation results and the vibration impact analysis results. The prediction formula for the adhesion quality of magnesium oxide is: , where exp() is the power of e, The prediction of magnesium oxide adhesion quality based on the particle adhesion evaluation and the vibration analysis results is based on the vibration effect coefficient, Lj, and Df, which comprehensively considers the various factors affecting magnesium oxide adhesion quality. The particle adhesion effect reflects the influence of the coating and particle characteristics on adhesion quality, while the vibration analysis results consider the dynamic influence of silicon steel vibration during the coating process. Combining these two results allows for a more accurate prediction of magnesium oxide adhesion quality, as silicon steel vibration can negatively impact magnesium oxide adhesion.

[0024] In one implementation of the present application, the step 5 performs an early warning of the adhesion effect based on the predicted result of the magnesium oxide adhesion quality, including the following specific contents:

[0025] The obtained magnesium oxide adhesion mass is compared with the set magnesium oxide adhesion mass threshold. If the magnesium oxide adhesion mass is greater than or equal to the set magnesium oxide adhesion mass threshold, it means that the adhesion effect meets the requirements and the coating operation is performed. If the magnesium oxide adhesion mass is less than the set magnesium oxide adhesion mass threshold, it means that the adhesion effect does not meet the requirements and the coating operation cannot be performed, and an operation warning is issued.

[0026] Secondly, in a second aspect, the present application further provides a detection system for the adhesion of magnesium oxide to silicon steel, which is implemented based on the above-mentioned detection method for the adhesion of magnesium oxide to silicon steel, and includes:

[0027] The data acquisition module is used to obtain the corresponding magnesium oxide coating particle conditions and silicon steel surface conditions through the visual module, and at the same time obtain the fluidity of the coating in the scene and the vibration of the silicon steel during the coating process; the matching analysis module performs particle matching analysis based on the corresponding magnesium oxide coating particle conditions and silicon steel surface conditions; the bonding effect evaluation module evaluates the particle bonding effect based on the particle matching analysis results and the fluidity of the coating in the scene; the adhesion quality prediction module predicts the magnesium oxide adhesion quality based on the particle adhesion effect evaluation results and the vibration of the equipment during the coating process; the operation output module issues an adhesion effect warning based on the prediction result of the magnesium oxide adhesion quality.

[0028] Furthermore, in a third aspect, the present application provides an electronic device comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a method for detecting the adhesion of magnesium oxide to silicon steel by calling the computer program stored in the memory.

[0029] Finally, in a fourth aspect, the present application provides a computer-readable storage medium storing instructions, which, when executed on a computer, causes the computer to execute a method for detecting magnesium oxide adhesion of silicon steel.

[0030] Compared with the prior art, this application has the following beneficial effects:

[0031] Based on the corresponding magnesium oxide coating particles and silicon steel surface conditions, a particle matching analysis is performed. Based on the particle matching analysis results and the fluidity of the coating in the scene, the particle bonding effect is evaluated. Based on the evaluation results of the particle bonding effect and the vibration of the equipment during the coating process, the magnesium oxide adhesion quality is predicted. Taking into account the coating characteristics, silicon steel surface conditions, coating environment and dynamic factors in the coating process, the magnesium oxide adhesion is evaluated and analyzed from multiple dimensions. This multi-dimensional analysis method provides a more scientific and accurate evaluation method for silicon steel coating technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0033] Figure 1 This is a schematic diagram of the overall process of Example 1 of the present application method;

[0034] Figure 2 This is a schematic flow chart of step S2 of Example 1 of the method of this application;

[0035] Figure 3 This is a structural diagram of embodiment 2 of the system of this application. DETAILED DESCRIPTION

[0036] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below in conjunction with the drawings in the specification.

[0037] In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0038] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present application. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it constitute a separate or selective embodiment that is mutually exclusive with other embodiments.

[0039] Example 1

[0040] like Figures 1 to 2 As shown, this embodiment provides a method for detecting the adhesion of magnesium oxide to silicon steel, which specifically includes the following steps:

[0041] Step 1: Use the visual module to obtain the corresponding magnesium oxide coating particle situation and silicon steel surface situation, and at the same time obtain the fluidity of the coating in the scene and the vibration of the silicon steel during the coating process;

[0042] In one specific embodiment, the condition of the magnesium oxide coating particles includes the size of the magnesium oxide particles in the coating and the viscosity of the liquid in the coating, wherein the size of the magnesium oxide particles and the viscosity of the liquid in the coating are obtained through experiments, and the surface condition of the silicon steel includes the size and shape data of the pits on the surface of the silicon steel, which are collected through a machine vision module. For example, the machine vision module can be a three-dimensional image acquisition module or a microscope, etc. The fluidity of the coating in the scene is obtained through a coating experiment, and the jitter of the silicon steel during the coating process is obtained through a jitter sensor to obtain the jitter of the silicon steel surface. The reason for collecting the jitter of the silicon steel surface is that during coating, the jitter will make it difficult for the coating to adhere to the silicon steel, and the jitter during coating will cause the coating to be unevenly distributed on the silicon steel surface. When the coating is unevenly distributed, the coating in some areas is too thick, and Some areas are too thin. A coating that is too thick may crack or peel during the drying process, while a coating that is too thin cannot provide sufficient adhesion, thereby affecting the overall bonding effect between the coating and the silicon steel. At the same time, shaking can easily cause air to mix into the coating to form bubbles. These bubbles will form an isolation layer between the coating and the silicon steel, hindering the full contact between the coating and the silicon steel surface and reducing the actual contact area between the two. The reduction in contact area will directly lead to a decrease in adhesion, making it difficult for the coating to adhere firmly to the silicon steel. Moreover, the adhesion between the coating and the silicon steel depends on the physical and chemical interactions between the coating molecules and the silicon steel surface, such as van der Waals forces and chemical bonds. Shaking during coating will interfere with the normal formation of this interaction. An unstable coating process will prevent the coating molecules from being adsorbed and attached to the silicon steel surface in an orderly manner, thereby weakening the adhesion.

[0043] Step 2: Perform particle matching analysis based on the corresponding magnesium oxide coating particle situation and silicon steel surface situation;

[0044] In one specific embodiment, the particle matching analysis in step 2 includes the following specific steps:

[0045] S21. Obtain the size of the magnesium oxide particles and import the size of the magnesium oxide particles into a size uniformity analysis formula for size uniformity analysis. The size here can be expressed by particle size or volume. The size uniformity analysis formula is: , where n is the number of particles, xi is the particle size of the i-th magnesium oxide particle, and xp is the average particle size of the magnesium oxide particles. The size uniformity analysis formula essentially measures the degree of dispersion of the particle size of each particle relative to the average particle size. When the particle size xi of each particle is closer to the average value xp, the result of the formula is closer to 0, indicating that the particle size is more uniform; conversely, the larger the result, the more dispersed the particle size.

[0046] S22. Obtain the size and shape data of the etch pits on the silicon steel surface, and import the size of the etch pits on the silicon steel surface into the etch pit uniformity analysis formula to perform etch pit uniformity analysis, wherein the etch pit uniformity analysis formula is: , where m is the number of pits on the silicon steel surface, sj is the size of the i-th pit on the silicon steel surface, and sp is the average size of the pits on the silicon steel surface;

[0047] S23. Based on the results of the pit uniformity analysis and the size uniformity analysis, and the similarity in shape between the magnesium oxide particles and the pits on the silicon steel surface, a particle matching analysis is performed. The particle matching analysis formula is: , where Vmin() is the minimum volume of the image in brackets, Vmax() is the maximum volume of the image in brackets, as is the image shape of the standard magnesium oxide particles, and cs is the image shape of the standard etch pit. is the intersection of two images, is the union of two images, Represents the maximum value of the intersection of the images divided by the minimum value of the union of the images, which represents the similarity of the two images. pc is the result of the size uniformity analysis, and ps is the result of the pit uniformity analysis. The sum of the two represents the effect of the size uniformity of the magnesium oxide particles and the pits on the silicon steel surface on the matching degree. By analyzing the matching of magnesium oxide particles and the pits on the silicon steel surface, we can understand the degree of fit between the two in terms of size, shape, etc. The sum of the two reflects the effect of size uniformity on the matching degree. Magnesium oxide particles and pits with uniform size are more conducive to achieving a good match, because uneven size may cause some particles to be unable to effectively fill the pits, thereby affecting the coating effect. By comprehensively considering shape similarity and size uniformity, the particle matching analysis formula can comprehensively evaluate the matching degree between magnesium oxide particles and pits on the silicon steel surface, and then analyze its impact on the coating effect.

[0048] Step 3: Evaluate the particle bonding effect based on the particle matching analysis results and the fluidity of the coating in the scene;

[0049] In one specific embodiment, the evaluation of the particle bonding effect in step 3 includes the following specific steps:

[0050] S31. Obtain the fluidity and viscosity of the coating in the scene, and simultaneously obtain the pitting condition of the silicon steel surface. Perform a silicon steel surface smoothness analysis based on the pitting condition to quantify the effect of the silicon steel surface smoothness on the fluidity of the coating. For example, the silicon steel surface smoothness analysis formula is: , where w is the number of monitoring points on the silicon steel surface, hc is the height of the cth silicon steel surface point, hs is the height of the silicon steel plane, and hz is the set height difference standard value. The silicon steel surface flatness analysis results and the fluidity of the coating in the scene are obtained to perform coating flow anomaly analysis. The fluidity of the coating in the scene is an indicator of the fluidity of the coating under temperature and humidity. The coating flow anomaly analysis formula is: , where Lz is the fluidity of the paint in the scene, which can be expressed by the flow velocity, and Lzm is the standard value of fluidity. The influence coefficient of silicon steel surface flatness on fluidity is obtained by obtaining the pitting conditions on the silicon steel surface and performing surface flatness analysis, which can quantitatively describe the flatness of the silicon steel surface. This helps to accurately understand the micromorphology of the silicon steel surface and provide a basis for subsequent research on the flow behavior of the coating on its surface. Different silicon steel surface flatness may cause changes in the flow path and speed of the coating on its surface. Therefore, it is very necessary to accurately evaluate the silicon steel surface. The results of the silicon steel surface flatness analysis are combined with the fluidity of the coating in the scene to perform coating flow anomaly analysis. The specific degree of influence of the silicon steel surface flatness on the coating flow can be clarified. The influence coefficient of silicon steel surface flatness on fluidity is obtained. This formula is based on the principles of fluid mechanics. The fluidity of the coating will be affected by multiple factors such as surface roughness, temperature, and humidity. The unevenness of the silicon steel surface will increase the resistance to the flow of the coating, resulting in a difference between the actual flow velocity of the coating and the standard flow velocity. By introducing the influence coefficient ,This difference can be quantified, thus conducting coating flow anomaly analysis;

[0051] S32. Obtain the coating flow anomaly analysis results, the coating viscosity, and the particle matching analysis results to evaluate the particle bonding effect. The particle bonding effect evaluation formula is: ,in, is the viscosity of the coating, For the standard viscosity case, is the influence coefficient of coating flow abnormality, The viscosity of the coating affects the coefficient. The results of the coating flow anomaly analysis, the coating viscosity, and the particle matching analysis are combined to evaluate the particle bonding effect. This can comprehensively consider various factors that affect the particle bonding effect. The fluidity, viscosity, and matching of the coating with the particles will directly affect the bonding strength and uniformity of the particles on the silicon steel surface. Therefore, a comprehensive evaluation can more accurately predict the particle bonding effect.

[0052] Step 4: Predict the adhesion quality of magnesium oxide based on the evaluation results of the particle bonding effect and the vibration of the equipment during the coating process;

[0053] In one specific embodiment, the prediction of the magnesium oxide adhesion quality in step 4 includes the following specific contents:

[0054] S41. Obtain the vibration of the silicon steel during the coating process. Based on the vibration of the silicon steel during the coating process, perform a vibration impact analysis on the adhesion of magnesium oxide based on the vibration amplitude and vibration frequency of the silicon steel surface. The vibration impact analysis formula is: , where G is the average number of vibrations during the monitoring period, Hg is the amplitude of the g-th vibration, and Hm is the safe amplitude of the vibration. The vibration of silicon steel during the coating process is obtained, and the influence of vibration on the adhesion of magnesium oxide is analyzed based on the vibration amplitude and vibration frequency. This can accurately quantify the influence of the dynamic factor of vibration on the adhesion effect of magnesium oxide. In the actual coating process, the vibration of silicon steel is inevitable, but different vibration amplitudes and frequencies will have different degrees of influence on the adhesion of magnesium oxide. Through this analysis, the degree of this influence can be accurately grasped. The vibration of silicon steel will generate inertial force, which will act on the magnesium oxide particles and affect their adhesion process on the silicon steel surface. The larger the vibration amplitude, the greater the inertial force, and the greater the interference with the adhesion of magnesium oxide. The more vibrations there are, the more frequently the magnesium oxide particles are disturbed. When the vibration amplitude exceeds the safe vibration amplitude, the magnesium oxide particles may not be firmly attached to the silicon steel surface due to excessive inertia, and may even fall off the silicon steel surface. Therefore, this formula can quantify the impact of vibration on magnesium oxide adhesion by comprehensively considering the relationship between the number of vibrations and the vibration amplitude and the safe vibration amplitude.

[0055] S42. Predicting the adhesion quality of magnesium oxide based on the particle bonding effect evaluation results and the vibration impact analysis results. The prediction formula for the adhesion quality of magnesium oxide is: , where exp() is the power of e, is the jitter influence coefficient, Lj is the particle adhesion effect, and Df is the jitter influence analysis result. The prediction of magnesium oxide adhesion quality based on the particle adhesion effect evaluation results and the jitter influence analysis results can comprehensively consider the various factors affecting the adhesion quality of magnesium oxide. The particle adhesion effect reflects the influence of the characteristics of the coating and the particles themselves on the adhesion quality, while the jitter influence analysis results consider the influence of the dynamic factor of silicon steel jitter during the coating process. Combining the results of these two aspects, the adhesion quality of magnesium oxide can be more accurately predicted. The jitter of silicon steel will have a negative impact on the adhesion of magnesium oxide. This formula can reasonably comprehensively consider the influence of these two factors by introducing the exponential function exp() and the jitter influence coefficient λ. The exponential function can better describe the nonlinear relationship between the factors, and the jitter influence coefficient can adjust the influence weight of the jitter factor on the adhesion quality of magnesium oxide according to the actual situation, thereby more accurately predicting the adhesion quality of magnesium oxide.

[0056] Step 5: providing an early warning of the adhesion effect based on the predicted result of the magnesium oxide adhesion quality;

[0057] In one specific embodiment, the following specific contents are included:

[0058] The obtained magnesium oxide adhesion mass is compared with the set magnesium oxide adhesion mass threshold. If the magnesium oxide adhesion mass is greater than or equal to the set magnesium oxide adhesion mass threshold, it means that the adhesion effect meets the requirements and the coating operation is performed. If the magnesium oxide adhesion mass is less than the set magnesium oxide adhesion mass threshold, it means that the adhesion effect does not meet the requirements and the coating operation cannot be performed. An operation warning is performed. For example, the warning method can be to send an alarm or text message to remind the staff.

[0059] It should be noted that the setting parameters in this embodiment are obtained by historical data experiments, and obtaining the values ​​of the setting parameters through historical data experiments is a conventional technique for those skilled in the art. The exemplary experimental method is: obtaining the historical corresponding magnesium oxide coating particle situation and silicon steel surface situation, and obtaining the fluidity of the corresponding historical coating in the scene and the jitter of the silicon steel during the coating process, and obtaining the judgment result of whether the magnesium oxide adhesion meets the requirements after the historical coating, importing the obtained historical data into each step of this embodiment, and finally obtaining the result of whether the adhesion effect meets the requirements, importing the judgment result of whether the requirements are met and the result of whether the adhesion effect meets the requirements into the matlaba fitting software for fitting, and obtaining the value of the setting parameter that meets the maximum judgment accuracy; a large amount of historical data contains information under various production conditions, and these data have certain statistical laws and probability distributions. For example, under different combinations of magnesium oxide coating particle conditions, silicon steel surface conditions, etc., the probability that the magnesium oxide adhesion effect meets the requirements is different. By performing statistical analysis and fitting on the historical data, the relationship between these probability distributions can be found, thereby determining the setting parameter value that can maximize the judgment accuracy. The historical data is collected from a large number of production cases and has a certain sample representativeness. By fitting these representative sample data, we can obtain universally applicable set parameters, enabling the detection system to accurately determine the adhesion effect of magnesium oxide under different production batches and conditions. Matlab fitting software has powerful fitting capabilities, capable of finding the most appropriate mathematical model to describe the relationship between the data based on the input data. By importing the judgment results and adhesion effect results from historical data into the fitting software for fitting, we can obtain a mathematical model that can accurately predict whether the magnesium oxide adhesion effect meets the requirements. The parameters in this model are the set parameters obtained through fitting, and they can reflect the quantitative relationship between various factors and adhesion effect.

[0060] It should be noted that in this embodiment, this embodiment has the following benefits: particle matching analysis is performed based on the corresponding magnesium oxide coating particle conditions and silicon steel surface conditions, particle bonding effect is evaluated based on the particle matching analysis results and the fluidity of the coating in the scene, magnesium oxide adhesion quality is predicted based on the evaluation results of the particle bonding effect and the vibration of the equipment during the coating process, and the coating characteristics, silicon steel surface conditions, coating environment and dynamic factors in the coating process are comprehensively considered to evaluate and analyze magnesium oxide adhesion from multiple dimensions. This multi-dimensional analysis method provides a more scientific and accurate evaluation method for silicon steel coating technology.

[0061] Example 2

[0062] like Figure 3 As shown, the present embodiment provides a detection system for the adhesion of magnesium oxide to silicon steel, which is implemented based on a detection method for the adhesion of magnesium oxide to silicon steel in Example 1, and includes: a data acquisition module, which is used to obtain the corresponding magnesium oxide coating particle situation and the silicon steel surface situation through a visual module, and at the same time obtain the fluidity of the coating in the scene and the jitter of the silicon steel during the coating process; a matching analysis module, which performs particle matching analysis based on the corresponding magnesium oxide coating particle situation and the silicon steel surface situation; a bonding effect evaluation module, which evaluates the particle bonding effect based on the particle matching analysis results and the fluidity of the coating in the scene; an adhesion quality prediction module, which predicts the adhesion quality of magnesium oxide based on the evaluation results of the particle bonding effect and the jitter of the equipment during the coating process; an operation output module, which performs adhesion effect warning based on the prediction results of the magnesium oxide adhesion quality, and also includes a control module, which controls the operation of other modules through a control terminal. The specific steps of each module of the embodiment of the present system are the same as the specific steps of the method embodiment of Example 1, and are not repeated here.

[0063] Example 3

[0064] An electronic device according to an embodiment of the present application includes a processor and a memory, wherein the memory stores a computer program that can be called by the processor. The processor executes a method for detecting the adhesion of magnesium oxide to silicon steel by calling the computer program stored in the memory. It should be noted that all computer programs of the method for detecting the adhesion of magnesium oxide to silicon steel are implemented in the C language.

[0065] Example 4

[0066] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0067] When the computer program is run on a computer device, the computer device is caused to execute the above-mentioned method for detecting magnesium oxide adhesion of silicon steel.

[0068] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. A computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0069] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0070] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0071] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only one type. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0072] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0073] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0074] Throughout this specification, references to terms such as "one embodiment," "example," and "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0075] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present application. Various changes and improvements may be made to the present application without departing from the spirit and scope of the present application. These changes and improvements are intended to fall within the scope of the present application. The scope of protection claimed in this application is defined by the appended claims and their equivalents.

Claims

1. A method for detecting the adhesion of magnesium oxide to silicon steel, characterized in that: The steps include: Step 1: Use the visual module to obtain the corresponding magnesium oxide coating particle situation and silicon steel surface situation, and at the same time obtain the fluidity of the coating in the scene and the vibration of the silicon steel during the coating process; Step 2: Perform particle matching analysis based on the corresponding magnesium oxide coating particle situation and silicon steel surface situation; Step 3: Evaluate the particle bonding effect based on the particle matching analysis results and the fluidity of the coating in the scene; The evaluation of the particle bonding effect includes the following specific steps: Obtain the fluidity and viscosity of the paint in the scene, and at the same time obtain the pitting situation on the silicon steel surface. Based on the pitting situation on the silicon steel surface, perform silicon steel surface flatness analysis to quantify the impact of silicon steel surface flatness on the fluidity of the paint. Obtain the silicon steel surface flatness analysis results and the fluidity of the paint in the scene to perform paint flow anomaly analysis; Obtain the coating flow anomaly analysis results, coating viscosity, and particle matching analysis results to evaluate the particle bonding effect. The particle bonding effect evaluation formula is: ,in, is the viscosity of the coating, For the standard viscosity case, is the influence coefficient of coating flow abnormality, is the coating viscosity influence coefficient, Js is the particle matching, and Ld is the coating flow abnormality analysis result; Step 4: Predict the adhesion quality of magnesium oxide based on the evaluation results of the particle bonding effect and the vibration of the equipment during the coating process; The prediction of the magnesium oxide adhesion quality includes the following specific contents: Obtain the vibration of silicon steel during the coating process, and analyze the impact of vibration on magnesium oxide adhesion based on the vibration amplitude and frequency of the silicon steel surface. The magnesium oxide adhesion quality is predicted based on the particle bonding effect evaluation results and the vibration impact analysis results. The prediction formula for magnesium oxide adhesion quality is: , where exp() is the power of e, is the jitter influence coefficient, Lj is the particle bonding effect, and Df is the jitter influence analysis result; Step 5: Provide an early warning of the adhesion effect based on the predicted result of the magnesium oxide adhesion quality.

2. A method for detecting the adhesion of magnesium oxide to silicon steel according to claim 1, characterized in that: The particle matching analysis in step 2 includes the following specific steps: Obtaining the size of the magnesium oxide particles and importing the size of the magnesium oxide particles into a size uniformity analysis formula for size uniformity analysis; Obtain the size and shape data of the silicon steel surface etch pits, and import the size of the silicon steel surface etch pits into the etch pit uniformity analysis formula to perform etch pit uniformity analysis; Based on the results of pit uniformity analysis and size uniformity analysis and the similarity in shape between magnesium oxide particles and pits on the silicon steel surface, a particle matching analysis was performed.

3. A method for detecting the adhesion of magnesium oxide to silicon steel according to claim 2, characterized in that: The particle matching analysis formula is: , where Vmin() is the minimum volume of the image in brackets, Vmax() is the maximum volume of the image in brackets, as is the image shape of the standard magnesium oxide particles, and cs is the image shape of the standard etch pit. is the intersection of two images, is the union of the two images, where pc is the size uniformity and ps is the pit uniformity.

4. The method for detecting the adhesion of magnesium oxide to silicon steel according to claim 1, wherein: The adhesion effect warning based on the prediction result of magnesium oxide adhesion quality includes the following specific contents: The obtained magnesium oxide adhesion mass is compared with the set magnesium oxide adhesion mass threshold. If the magnesium oxide adhesion mass is greater than or equal to the set magnesium oxide adhesion mass threshold, it means that the adhesion effect meets the requirements and the coating operation is performed. If the magnesium oxide adhesion mass is less than the set magnesium oxide adhesion mass threshold, it means that the adhesion effect does not meet the requirements and the coating operation cannot be performed, and an operation warning is issued.

5. The method for detecting the adhesion of magnesium oxide to silicon steel according to claim 1, wherein: The magnesium oxide coating particle condition includes the size of the magnesium oxide particles in the coating and the viscosity of the liquid in the coating. The silicon steel surface condition includes the size and shape data of the etch pits on the silicon steel surface. The jitter condition of the silicon steel during the coating process is obtained by a jitter sensor.

6. A system for detecting the adhesion of magnesium oxide to silicon steel, which is implemented based on a method for detecting the adhesion of magnesium oxide to silicon steel according to any one of claims 1 to 5, characterized in that: The system comprises: The data acquisition module is used to obtain the corresponding magnesium oxide coating particle conditions and silicon steel surface conditions through the visual module, and at the same time obtain the fluidity of the coating in the scene and the vibration of the silicon steel during the coating process; the matching analysis module performs particle matching analysis based on the corresponding magnesium oxide coating particle conditions and silicon steel surface conditions; the bonding effect evaluation module evaluates the particle bonding effect based on the particle matching analysis results and the fluidity of the coating in the scene; the adhesion quality prediction module predicts the magnesium oxide adhesion quality based on the particle adhesion effect evaluation results and the vibration of the equipment during the coating process; the operation output module issues an adhesion effect warning based on the prediction result of the magnesium oxide adhesion quality.

7. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes a method for detecting magnesium oxide adhesion of silicon steel according to any one of claims 1 to 5 by calling the computer program stored in the memory.

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