Data Analysis-Based Quality Control System and Method for Magnesium Oxide Coating on Silicon Steel Surface

Abstract

The present application relates to the production environment monitoring technical field, especially to the silicon steel surface magnesium oxide coating quality control system and method based on data analysis, in the magnesium oxide adhesion prediction, the shape, size matching and environmental factors of silicon steel surface pit and magnesium oxide particle are comprehensively considered, the prediction deviation caused by single factor is avoided;Coating effect analysis combines the friction collision condition and coating adhesion of the history use of silicon steel, and evaluates the dynamic risk;Coating thickness analysis accurately calculates the required coating thickness according to the coating effect threshold value;Finally, the required coating thickness is compared with the safety range, coating meets the process requirements and conforms to the actual operation range, avoids production risk from the source, and guarantees product quality and process feasibility.

Description

Technical Field

[0001] This invention relates to the field of production environment monitoring technology, and in particular to a quality control system and method for magnesium oxide coating on silicon steel surfaces based on data analysis. Background Technology

[0002] To further improve the performance of silicon steel, coating its surface with magnesium oxide is a widely used process. Magnesium oxide coating plays several important roles. It forms an insulating film on the silicon steel surface, effectively reducing eddy current losses between silicon steel sheets and improving the electrical insulation performance of the silicon steel. Simultaneously, the magnesium oxide coating prevents rust and corrosion on the silicon steel surface, extending its service life. Furthermore, during high-temperature annealing, the magnesium oxide coating acts as a release agent, preventing the silicon steel sheets from sticking together and ensuring the flatness and surface quality of the silicon steel sheets.

[0003] However, controlling the quality of magnesium oxide coating on silicon steel surfaces still faces many challenges. Existing coating quality control methods often focus on single factors, lacking comprehensive analysis of multiple factors such as the surface condition of silicon steel, the usage environment, and the coating process. For example, when determining the thickness of magnesium oxide coating, traditional methods usually rely solely on empirical formulas or simple experimental data, without fully considering the differences in the microstructure of the silicon steel surface and the influence of environmental factors during the coating process. This single-factor control method easily leads to inaccurate coating thickness, thus affecting the performance of silicon steel. In terms of predicting magnesium oxide adhesion, existing technologies often only focus on the roughness of the silicon steel surface, ignoring the matching relationship between the shape and size of magnesium oxide particles and the pits on the silicon steel surface, as well as the influence of environmental factors on the adhesion effect. This makes the magnesium oxide coating weakly adhered during the actual coating process, prone to peeling, and reducing the protective performance of silicon steel. In addition, existing coating effect analysis methods mainly focus on static evaluation, without fully considering the impact of friction and collision on the coating during actual use. Silicon steel is inevitably subjected to friction and collision during transportation, installation, and use, which may damage the coating and affect the performance and service life of silicon steel. Existing coating effect analysis methods cannot accurately assess this dynamic risk and are unable to provide effective quality control measures. Summary of the Invention

[0004] In order to overcome the defects and shortcomings of the existing technology, the present invention provides a quality control system and method for magnesium oxide coating on silicon steel surface based on data analysis.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] In a first aspect, the present invention provides a method for quality control of magnesium oxide coating on silicon steel surfaces based on data analysis, comprising the following steps:

[0007] Step S1: Obtain the surface condition of silicon steel and usage data, the environmental conditions of magnesium oxide coating and the corresponding magnesium oxide coating conditions;

[0008] Step S2: Predict the coating condition based on the surface condition of silicon steel and the corresponding magnesium oxide coating condition, and predict the magnesium oxide adhesion based on the coating condition prediction results and the magnesium oxide coating environment.

[0009] Step S3: Analyze the effect of magnesium oxide coating based on the magnesium oxide adhesion prediction results and the usage data of silicon steel during use;

[0010] Step S4: Analyze the coating thickness on the silicon steel surface based on the results of the magnesium oxide coating effect analysis;

[0011] Step S5: Apply magnesium oxide to the silicon steel surface according to the corresponding coating thickness.

[0012] In one implementation of the present invention, the silicon steel surface condition includes a three-dimensional image of the silicon steel surface, acquired by a laser profilometer; the silicon steel usage data includes the force and number of frictions and collisions during the use of the silicon steel, acquired by corresponding silicon steel usage records; the magnesium oxide coating environment condition includes environmental data affecting the coating quality, such as temperature and humidity, acquired by environmental sensors; and the corresponding magnesium oxide coating condition includes the shape, uniformity, and adhesion strength of the coating particles, acquired by obtaining corresponding data through magnesium oxide coating experiments, where the shape refers to the three-dimensional shape of the magnesium oxide particles and the uniformity refers to the uniformity of the particle size of the magnesium oxide particles.

[0013] In one implementation of the present invention, the abnormal prediction of magnesium oxide adhesion in step S2 includes the following specific steps:

[0014] S21. Obtain the three-dimensional image data of the corresponding silicon steel surface, including the shape and size of the pits on the silicon steel surface, and the shape and size of the magnesium oxide particles. The size here is preferably the volume of the particles and the pits. By accurately measuring the volume of the pits and magnesium oxide particles on the silicon steel surface using three-dimensional imaging technology (such as laser scanning or optical profilometer), the microstructure of the coated substrate can be characterized more intuitively, rather than relying solely on the two-dimensional projection size. The volume data can better reflect the actual coating contact area, improving the accuracy of subsequent matching analysis. The coating quality is directly related to the degree of filling of the magnesium oxide particles in the pits, and the filling effect is determined by the matching of the shape and volume of the particles and the pits. Therefore, obtaining three-dimensional data is more valuable than traditional two-dimensional measurement.

[0015] S22. The shape matching degree is analyzed by the shape of the pits on the silicon steel surface and the shape of the magnesium oxide particles. At the same time, the matching uniformity analysis is performed by averaging the uniformity of the size of the pits on the silicon steel surface and the uniformity of the size of the magnesium oxide particles. The coating uniformity prediction analysis result is obtained by weighted summing the shape matching degree analysis result and the matching uniformity analysis result.

[0016] The uniformity of pit size and magnesium oxide particle size can be calculated using the standard deviation formula. Then, the uniformity of the corresponding data can be obtained by subtracting the standard deviation calculated by the corresponding standard deviation formula from 1. The standard deviation reflects the dispersion of the data, and 1-standard deviation can be converted into a uniformity index.

[0017] Meanwhile, the method for analyzing the degree of shape matching is as follows: obtain the shape of the standard pit and the shape of the corresponding standard magnesium oxide particle, and obtain the degree of shape matching by dividing the volume of the intersection of the two shapes by the volume of the union of the two shapes. This can quantitatively assess the geometric compatibility between magnesium oxide particles and pits, avoiding reliance on experience alone.

[0018] Furthermore, the weights for the weighted summation here are obtained through experiments using historical data.

[0019] S23. Obtain environmental data affecting coating quality during the coating process. Set the standard deviation of each environmental data point from the corresponding safe range of the environmental data as the outlier of the corresponding environmental data. Sum the outliers of all types of environmental data by weight to obtain the environmental outlier. It should be noted that the standard deviation here is the difference between the environmental data and the median of the safe range divided by the difference of the safe range. The weighting weights are obtained through experiments. Obtain the impact of each environmental change on the coating quality. Allow for flexible adjustment of the sensitivity to environmental fluctuations.

[0020] S24. Multiply the obtained environmental anomaly value by the environmental impact coefficient to obtain the environmental impact result. Multiply the difference between 1 and the environmental impact result by the coating uniformity prediction analysis result to obtain the magnesium oxide adhesion prediction result.

[0021] In one implementation of the present invention, the analysis of the magnesium oxide coating effect in step S3 includes the following specific contents:

[0022] S31. Obtain the average force and frequency of friction and collision during the historical use of the corresponding silicon steel, as well as the adhesion force after the corresponding magnesium oxide coating is bonded. Obtain the detachment force anomaly by dividing the average force of friction and collision during the historical use of the corresponding silicon steel by the adhesion force after the corresponding magnesium oxide coating is bonded. Obtain the detachment frequency anomaly by the ratio of the frequency of friction and collision during use to the safe frequency. Obtain the adhesion detachment anomaly by multiplying the detachment force anomaly by the detachment frequency anomaly. The adhesion detachment anomaly assesses its dynamic risk by quantifying the degree of damage to the coating by external force.

[0023] S32. Obtain the reciprocal of the adhesion detachment anomaly to get the adhesion fixation coefficient. To avoid the denominator being 0 and the reciprocal being meaningless, a very small constant needs to be added to the denominator. The adhesion fixation coefficient and the magnesium oxide adhesion prediction result are weighted and summed to obtain the magnesium oxide coating effect.

[0024] In one implementation of the present invention, the analysis of coating thickness in step S4 includes the following specific details:

[0025] S41. Obtain the magnesium oxide coating effect threshold and the coating thickness corresponding to the threshold, and at the same time obtain the magnesium oxide coating effect obtained from the analysis.

[0026] S42. The required magnesium oxide coating thickness for silicon steel is obtained by multiplying the ratio obtained by dividing the magnesium oxide coating effect threshold by the corresponding magnesium oxide coating effect and then multiplying the ratio by the coating thickness corresponding to the threshold. In industrial production, the qualified standard for magnesium oxide coating effect is usually determined by experiments or industry specifications. For example, the coating effect of a certain silicon steel product must reach the threshold to be considered qualified, and the corresponding ideal coating thickness is 5μm±0.5μm. If the actual test finds that the coating effect does not meet the standard, the process can be adjusted by calculating the compensation thickness: First, divide the threshold by the actual effect to obtain the compensation coefficient, and then multiply this coefficient by the standard thickness to determine the new target coating thickness. This method can not only accurately repair defective batches, but also optimize the production process.

[0027] In one implementation of the present invention, step S5, which involves coating the silicon steel surface with magnesium oxide according to the corresponding coating thickness, includes the following specific details:

[0028] The required magnesium oxide coating thickness for the corresponding silicon steel is compared with the safe range of the actual coating thickness used. If the required magnesium oxide coating thickness for the corresponding silicon steel is within the safe range of the actual coating thickness used, then the coating is performed according to the required magnesium oxide coating thickness for the corresponding silicon steel. If the required magnesium oxide coating thickness for the corresponding silicon steel is not within the safe range of the actual coating thickness used, then a message is displayed indicating that coating cannot be performed, and the silicon steel or magnesium oxide coating needs to be replaced. The safe range of the actual coating thickness used here is obtained according to the actual scenario or according to the safe coating thickness of the coating equipment. The advantage of this comparison method is that it ensures that the magnesium oxide coating meets the process requirements.

[0029] Secondly, the present invention also provides a data analysis-based quality control system for magnesium oxide coating on silicon steel surfaces, comprising:

[0030] The data acquisition module acquires information on the surface condition of silicon steel, usage data, the environmental conditions of magnesium oxide coating, and the corresponding magnesium oxide coating condition.

[0031] The magnesium oxide adhesion prediction module predicts the coating condition based on the surface condition of silicon steel and the corresponding magnesium oxide coating condition, and then predicts the magnesium oxide adhesion based on the coating condition prediction results and the magnesium oxide coating environment.

[0032] The coating effect analysis module analyzes the effect of magnesium oxide coating by using the magnesium oxide adhesion prediction results and the usage data of silicon steel during the application process.

[0033] The coating thickness analysis module analyzes the coating thickness on the silicon steel surface based on the results of the magnesium oxide coating effect analysis.

[0034] The coating determination module coats the silicon steel surface with magnesium oxide according to the corresponding coating thickness of the silicon steel surface.

[0035] Thirdly, the present invention 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 data analysis-based method for quality control of magnesium oxide coating on silicon steel surfaces by calling the computer program stored in the memory.

[0036] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a data analysis-based method for quality control of magnesium oxide coating on silicon steel surfaces.

[0037] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0038] The solution comprehensively acquires data on the silicon steel surface, usage, coating environment, and coating itself. Through steps such as coating condition prediction, magnesium oxide adhesion prediction, coating effect analysis, and coating thickness analysis, it accurately determines the magnesium oxide coating thickness on the silicon steel surface. In magnesium oxide adhesion prediction, it comprehensively considers the shape and size matching of pits on the silicon steel surface and magnesium oxide particles, as well as environmental factors, to avoid prediction deviations caused by a single factor. The coating effect analysis combines the historical friction and collision data of the silicon steel and the coating adhesion to assess dynamic risks. The coating thickness analysis accurately calculates the required coating thickness based on the coating effect threshold. Finally, the required coating thickness is compared with the safe range to ensure that the coating meets both process requirements and actual operating conditions, mitigating production risks from the source and ensuring product quality and process feasibility. Attached Figure Description

[0039] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0040] Figure 1 This is a schematic diagram of the overall process of Embodiment 1 of the method of the present invention;

[0041] Figure 2 This is a schematic diagram of step S2 of embodiment 1 of the method of the present invention;

[0042] Figure 3 This is a schematic diagram of the structure of embodiment 2 of the system of the present invention;

[0043] Figure 4 This is a schematic diagram illustrating the acquisition of coating uniformity prediction analysis results in this invention. Detailed Implementation

[0044] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0045] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0046] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0047] Example 1

[0048] like Figures 1 to 2 As shown, this embodiment provides a data analysis-based method for quality control of magnesium oxide coating on silicon steel surfaces, specifically including the following steps:

[0049] Step S1: Obtain the surface condition of silicon steel and usage data, the environmental conditions of magnesium oxide coating and the corresponding magnesium oxide coating conditions;

[0050] In this embodiment, it should be noted that the silicon steel surface condition includes a three-dimensional image of the silicon steel surface, obtained through a laser profilometer; the silicon steel usage data includes the force and number of friction and collisions during the silicon steel usage process, obtained through corresponding silicon steel usage records; the magnesium oxide coating environment condition includes environmental data affecting coating quality, such as temperature and humidity, obtained through environmental sensors; and the corresponding magnesium oxide coating condition includes the shape and uniformity of the coating particles, as well as the adhesion after bonding, obtained through corresponding magnesium oxide coating experiments. The shape refers to the three-dimensional shape of the magnesium oxide particles, and the uniformity refers to the uniformity of the particle size. All data in this embodiment are stored in corresponding storage components.

[0051] Step S2: Predict the coating condition based on the surface condition of silicon steel and the corresponding magnesium oxide coating condition, and predict the magnesium oxide adhesion based on the coating condition prediction results and the magnesium oxide coating environment.

[0052] In this embodiment, it should be noted that the abnormal prediction of magnesium oxide adhesion in step S2 includes the following specific steps:

[0053] S21. Obtain the three-dimensional image data of the corresponding silicon steel surface, including the shape and size of the pits on the silicon steel surface, and the shape and size of the magnesium oxide particles. The size here is preferably the volume of the particles and the pits. By accurately measuring the volume of the pits and magnesium oxide particles on the silicon steel surface using three-dimensional imaging technology (such as laser scanning or optical profilometer), the microstructure of the coated substrate can be characterized more intuitively, rather than relying solely on the two-dimensional projection size. The volume data can better reflect the actual coating contact area, improving the accuracy of subsequent matching analysis. The coating quality is directly related to the degree of filling of the magnesium oxide particles in the pits, and the filling effect is determined by the matching of the shape and volume of the particles and the pits. Therefore, obtaining three-dimensional data is more valuable than traditional two-dimensional measurement.

[0054] S22, such as Figure 4 As shown, the shape matching degree is analyzed by the shape of the pits on the silicon steel surface and the shape of the magnesium oxide particles. At the same time, the matching uniformity analysis is performed by averaging the uniformity of the size of the pits on the silicon steel surface and the uniformity of the size of the magnesium oxide particles. The coating uniformity prediction analysis result is obtained by weighted summing the shape matching degree analysis result and the matching uniformity analysis result.

[0055] The uniformity of pit size and magnesium oxide particle size can be calculated using the standard deviation formula. Then, the uniformity of the corresponding data can be obtained by subtracting the standard deviation calculated by the corresponding standard deviation formula from 1. The standard deviation reflects the dispersion of the data, and 1-standard deviation can be converted into a uniformity index.

[0056] Meanwhile, the method for analyzing the degree of shape matching is as follows: obtain the shape of the standard pit and the shape of the corresponding standard magnesium oxide particle, and obtain the degree of shape matching by dividing the volume of the intersection of the two shapes by the volume of the union of the two shapes. This can quantitatively assess the geometric compatibility of magnesium oxide particles and pits, avoiding reliance on experience alone. Here, the standard pit and standard magnesium oxide particle are the mode in statistics, that is, the shape with the most numbers among the pits and magnesium oxide particles.

[0057] Furthermore, the weights for the weighted summation here are obtained through experiments using historical data.

[0058] S23. Obtain environmental data affecting coating quality during the coating process. Set the standard deviation of each environmental data point from the corresponding safe range of the environmental data as the outlier of the corresponding environmental data. Sum the outliers of all types of environmental data with weights to obtain the environmental outlier. It should be noted that the standard deviation here is the difference between the environmental data and the median of the safe range divided by the difference of the safe range. The weights are obtained through experiments. The impact of each environmental change on the coating quality is obtained separately. The sensitivity of environmental fluctuations can be flexibly adjusted. For example, if the coating process is sensitive to humidity, its coefficient can be increased to make the prediction more in line with actual needs. The comprehensive formula (uniformity prediction × environmental correction) considers both substrate-particle matching (S22) and environmental interference (S23) to avoid prediction deviations caused by a single factor. For example, even if the particles and pits match well, abnormal environmental temperature may still lead to a decrease in coating quality. This formula can effectively capture such situations.

[0059] S24. Multiply the obtained environmental anomaly value by the environmental impact coefficient to obtain the environmental impact result, and multiply the difference between 1 and the environmental impact result by the coating uniformity prediction analysis result to obtain the magnesium oxide adhesion prediction result.

[0060] Step S3: Analyze the effect of magnesium oxide coating based on the magnesium oxide adhesion prediction results and the usage data of silicon steel during use;

[0061] In this embodiment, it should be noted that the analysis of the magnesium oxide coating effect in step S3 includes the following specific contents:

[0062] S31. Obtain the average force and frequency of friction and collision during the historical use of the corresponding silicon steel, as well as the adhesion strength after the corresponding magnesium oxide coating is bonded. The detachment force anomaly is obtained by dividing the average force of friction and collision during the historical use of the corresponding silicon steel by the adhesion strength after the corresponding magnesium oxide coating is bonded. The detachment frequency anomaly is obtained by the ratio of the frequency of friction and collision during use to the safe frequency. The adhesion detachment anomaly is obtained by multiplying the detachment force anomaly by the detachment frequency anomaly. The adhesion detachment anomaly assesses its dynamic risk by quantifying the degree of damage to the coating caused by external forces. The specific calculation is divided into two steps: using historical friction or collision forces (such as those during transportation)... The ratio of the impact force (and equipment vibration stress) to the adhesion strength of the magnesium oxide coating (such as the peel strength measured in the laboratory) is calculated. If the ratio is >1, it indicates that the external force has exceeded the coating adhesion, which is likely to cause peeling. If the ratio is <1, the coating is more stable. The ratio of the actual friction / collision frequency to the safe frequency (such as the number of withstand cycles specified by industry standards) is also calculated. Although the damage caused by a single high-frequency, small external force is small, the cumulative fatigue effect will accelerate the failure of the coating. The final outlier is the product of the two, which comprehensively reflects the static destructive force and the dynamic wear risk. For example, if a silicon steel experiences frequent minor collisions during transportation (abnormally high frequency), even if the force of a single collision is small (abnormally low force), the product may still indicate a high risk.

[0063] S32. Obtain the reciprocal of the adhesion detachment anomaly to get the adhesion fixation coefficient. To avoid the denominator being 0 and the reciprocal being meaningless, a very small constant needs to be added to the denominator. The adhesion fixation coefficient and the magnesium oxide adhesion prediction result are weighted and summed to obtain the magnesium oxide coating effect.

[0064] Step S4: Analyze the coating thickness on the silicon steel surface based on the results of the magnesium oxide coating effect analysis;

[0065] In this embodiment, it should be noted that the analysis of coating thickness in step S4 includes the following specific details:

[0066] S41. Obtain the magnesium oxide coating effect threshold and the coating thickness corresponding to the threshold, and at the same time obtain the magnesium oxide coating effect obtained from the analysis.

[0067] S42. The required magnesium oxide coating thickness for silicon steel is obtained by multiplying the ratio obtained by dividing the magnesium oxide coating effect threshold by the corresponding magnesium oxide coating effect and the coating thickness corresponding to the threshold. In industrial production, the qualified standard for magnesium oxide coating effect is usually determined by experiments or industry specifications. For example, the coating effect of a certain silicon steel product must reach the threshold to be considered qualified, and the corresponding ideal coating thickness is 5μm±0.5μm. If the actual test finds that the coating effect does not meet the standard, the process can be adjusted by calculating the compensation thickness: First, divide the threshold by the actual effect to obtain the compensation coefficient, and then multiply this coefficient by the standard thickness to determine the new target coating thickness. This method can not only accurately repair defective batches, but also optimize the production process.

[0068] Step S5: Apply magnesium oxide coating to the silicon steel surface according to the corresponding coating thickness.

[0069] In this embodiment, it should be noted that step S5 involves coating the silicon steel surface with magnesium oxide according to the corresponding coating thickness, which includes the following specific details:

[0070] The required magnesium oxide coating thickness for the corresponding silicon steel is compared with the actual safe coating thickness range. If the required thickness falls within this range, coating is performed according to the specified thickness. If the required thickness does not, coating is not permitted, and either the silicon steel or the magnesium oxide coating needs to be replaced. The actual safe coating thickness range is determined based on the specific application scenario or the safe coating thickness of the coating equipment. This comparison method ensures that the magnesium oxide coating meets both process requirements (such as insulation and adhesion) and the actual safe operating range of the equipment or coating, avoiding [further issues]. The reason why coating failure or equipment damage may occur due to thickness deviation is that the coating effect of silicon steel is correlated with thickness (such as experimental data or industry standards), while the safe coating range of equipment or coating is determined by physical properties (such as flowability and drying speed) or equipment parameters (such as spraying accuracy and load-bearing capacity). By dynamically matching the required thickness with the safe range, product quality (such as achieving the standard 0.75 effect) and process feasibility (such as controlling the thickness within 4.5-7μm; if it exceeds the safe range, it will prompt to replace materials or adjust the process, thus avoiding production risks from the source. For example, if a silicon steel requires 6.25μm but the equipment's safe upper limit is 6μm, the system will directly terminate the coating and prompt to replace with a high-concentration coating or upgrade the equipment, rather than forcibly executing the process and causing the coating to crack.

[0071] It should be further explained that the setting parameters in this embodiment, such as the weighting weights and thresholds, are obtained as follows: For the weighting of the weighted summation of the coating uniformity prediction analysis results in step S22, a large amount of historical data on silicon steel coating is collected. The actual influence of different shape matching degrees and matching uniformity on coating uniformity is analyzed in the historical data. Statistical analysis methods, such as regression analysis, are used to determine the reasonable proportion of the two in the final result, thereby obtaining the weighting weights. For the weighting of the weighted summation of environmental data anomalies in step S23, a series of experiments are conducted to change different environmental factors (such as temperature, humidity, etc.), observe and quantify the impact of changes in each environmental factor on coating quality, and determine the weight of each environmental data in the weighted summation based on the degree of influence. As for the magnesium oxide coating effect threshold in step S41, multiple sets of silicon steel coating experiments are conducted to simulate different coating conditions and effects. Combined with the qualified standards of industrial production, industry specifications, and actual product usage requirements, a coating effect value that can ensure the silicon steel coating quality meets the expected requirements is determined as the threshold.

[0072] In this embodiment, it is important to note that it offers the following advantages: It acquires data on the silicon steel surface, usage, coating environment, and coating itself. Through steps such as coating condition prediction, magnesium oxide adhesion prediction, coating effect analysis, and coating thickness analysis, it accurately determines the magnesium oxide coating thickness on the silicon steel surface. In the magnesium oxide adhesion prediction, it comprehensively considers the shape and size matching of pits on the silicon steel surface and magnesium oxide particles, as well as environmental factors, avoiding prediction deviations caused by a single factor. The coating effect analysis combines the historical friction and collision conditions of the silicon steel with the coating adhesion to assess dynamic risks. The coating thickness analysis accurately calculates the required coating thickness based on the coating effect threshold. Finally, it compares the required coating thickness with the safe range to ensure that the coating meets both process requirements and actual operating conditions, mitigating production risks from the source and guaranteeing product quality and process feasibility.

[0073] Example 2

[0074] like Figure 3 As shown, this embodiment provides a data analysis-based quality control system for magnesium oxide coating on silicon steel surfaces, implemented based on the data analysis-based quality control method for magnesium oxide coating on silicon steel surfaces in Embodiment 1. The system includes: a data acquisition module for acquiring silicon steel surface conditions and usage data, magnesium oxide coating environment conditions, and the corresponding magnesium oxide coating condition; a magnesium oxide adhesion prediction module for predicting the coating condition based on the silicon steel surface conditions and the corresponding magnesium oxide coating condition, and predicting magnesium oxide adhesion based on the coating condition prediction results and the magnesium oxide coating environment conditions; and a coating effect analysis module for analyzing the magnesium oxide coating effect based on the magnesium oxide adhesion prediction results and usage data during the silicon steel usage process.

[0075] The coating thickness analysis module analyzes the coating thickness of the silicon steel surface based on the results of the magnesium oxide coating effect analysis; the coating determination module coats the silicon steel surface with magnesium oxide according to the corresponding coating thickness of the silicon steel surface. The specific steps of each module in this embodiment are the same as those in the method embodiment of embodiment 1, and will not be repeated here. At the same time, the operation of each module is controlled by a corresponding control module.

[0076] Example 3

[0077] An electronic device according to an embodiment of the present invention includes a processor and a memory, wherein the memory stores a computer program that can be called by the processor. The processor executes a data analysis-based method for quality control of magnesium oxide coating on silicon steel surfaces by calling the computer program stored in the memory. It should be noted that all computer programs for the data analysis-based method for quality control of magnesium oxide coating on silicon steel surfaces are implemented using the C programming language.

[0078] Example 4

[0079] This embodiment proposes a computer-readable storage medium on which an erasable and rewritable computer program is stored.

[0080] When the computer program runs on the computer device, it causes the computer device to perform the above-mentioned data analysis-based quality control method for magnesium oxide coating on silicon steel surfaces.

[0081] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is 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 transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network and / or wireless network. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of 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 (SSDs).

[0082] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0083] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0084] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only one method, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0085] The units described as separate components may or may not be physically separate. The 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 the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0086] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0087] In the description of this specification, references to terms such as "an embodiment," "example," and "specific example" indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions 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 one or more embodiments or examples.

[0088] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for controlling the quality of a magnesium oxide coating on the surface of silicon steel based on data analysis, characterized by, It comprises the following steps: Step S1, obtaining the surface condition of silicon steel and the use data, the magnesium oxide coating environment condition and the corresponding magnesium oxide coating condition; Step S2, predicting the coating condition by the surface condition of silicon steel and the corresponding magnesium oxide coating condition, and predicting the magnesium oxide adhesion by the coating condition prediction result and the magnesium oxide coating environment condition; The magnesium oxide adhesion prediction in step S2 comprises the following specific steps: S21, obtaining the three-dimensional image condition data of the corresponding silicon steel surface, obtaining the shape and size of the silicon steel surface pit, and obtaining the shape and size of the magnesium oxide particles; S22, analyzing the shape matching degree by the shape of the silicon steel surface pit and the shape of the magnesium oxide particles, and analyzing the matching uniformity by averaging the uniformity of the size of the silicon steel surface pit and the uniformity of the size of the magnesium oxide particles, and obtaining the coating uniformity prediction analysis result by weighted sum of the shape matching degree analysis result and the matching uniformity analysis result; S23, obtaining the environmental data affecting the coating quality in the coating process, setting the standard deviation of each environmental data and the corresponding environmental data coating safety range as the abnormal value of the corresponding environmental data, and obtaining the environmental abnormal value by weighted sum of the abnormal values of all kinds of environmental data; S24, multiplying the obtained environmental abnormal value by the environmental influence coefficient to obtain the environmental influence result, and multiplying the difference between 1 and the environmental influence result by the coating uniformity prediction analysis result to obtain the magnesium oxide adhesion prediction result; Step S3, analyzing the magnesium oxide coating effect by the magnesium oxide adhesion prediction result and the use data in the use process of silicon steel; Step S4, analyzing the coating thickness of the silicon steel surface by the magnesium oxide coating effect analysis result; Step S5, coating the magnesium oxide on the silicon steel surface according to the coating thickness of the corresponding silicon steel surface.

2. The data analysis based coating quality control method of claim 1, wherein, The magnesium oxide coating effect analysis in step S3 comprises the following specific contents: S31, obtaining the average force and frequency of friction and collision in the corresponding historical use process of silicon steel, and obtaining the bonding force of the corresponding magnesium oxide coating after bonding, obtaining the detachment force anomaly by dividing the average force of friction and collision in the corresponding historical use process of silicon steel by the bonding force of the corresponding magnesium oxide coating after bonding, obtaining the detachment frequency anomaly by the ratio of the frequency of friction and collision in the use process to the safe frequency, and obtaining the adhesion detachment anomaly by multiplying the detachment force anomaly and the detachment frequency anomaly; S32, obtaining the reciprocal of the adhesion detachment anomaly to obtain the adhesion fixation coefficient, and obtaining the magnesium oxide coating effect by weighted sum of the adhesion fixation coefficient and the magnesium oxide adhesion prediction result.

3. The data analysis based coating quality control method of claim 2, wherein, The coating thickness analysis in step S4 comprises the following specific contents: S41, obtaining the magnesium oxide coating effect threshold value and the coating thickness condition corresponding to the threshold value, and obtaining the magnesium oxide coating effect analyzed; S42, obtaining the ratio by dividing the magnesium oxide coating effect threshold value by the corresponding magnesium oxide coating effect, and obtaining the magnesium oxide coating thickness required by the corresponding silicon steel by multiplying the ratio by the coating thickness condition corresponding to the threshold value.

4. The data analysis based coating quality control method of claim 3, wherein, The coating of magnesium oxide on the silicon steel surface according to the coating thickness of the corresponding silicon steel surface in step S5 comprises the following specific contents: The magnesium oxide coating thickness required by the corresponding silicon steel is compared with the actual used coating thickness safety range. If the magnesium oxide coating thickness required by the corresponding silicon steel is within the actual used coating thickness safety range, the magnesium oxide coating thickness required by the corresponding silicon steel is coated. If the magnesium oxide coating thickness required by the corresponding silicon steel is not within the actual used coating thickness safety range, it is prompted that coating cannot be performed, and the silicon steel or magnesium oxide coating needs to be replaced.

5. The data analysis based coating quality control method of claim 1, wherein, The uniformity of the pit size and the uniformity of the size of the magnesium oxide particles are calculated by a standard deviation formula, and then the uniformity of the corresponding type of data is obtained by subtracting the standard deviation calculated by the corresponding standard deviation formula from 1. The shape matching degree is analyzed by obtaining the shape of the standard pit and the shape of the corresponding standard magnesium oxide particle, and the shape matching degree is obtained by dividing the volume of the intersection of the two shapes by the volume of the union of the two shapes.

6. The data analysis based coating quality control method of claim 1, wherein, The silicon steel surface condition includes a three-dimensional image condition of the silicon steel surface, which is obtained by a laser profiler. The silicon steel use data includes the intensity and frequency of friction and collision during the use of the silicon steel, which is obtained by recording the use process of the corresponding silicon steel. The magnesium oxide coating environment condition includes environmental data affecting the coating quality during the coating process, which is obtained by an environmental sensor. The corresponding magnesium oxide coating condition is the shape, uniformity condition, and bonding force condition of the coating particles after bonding.

7. A data analysis based coating quality control system for silicon steel surface magnesium oxide for implementing the data analysis based coating quality control method for silicon steel surface magnesium oxide according to any one of claims 1 to 6, characterized in that, The system comprises: A data acquisition module acquires the silicon steel surface condition, use data, magnesium oxide coating environment condition, and corresponding magnesium oxide coating condition. A magnesium oxide adhesion prediction module predicts the coating condition based on the silicon steel surface condition and the corresponding magnesium oxide coating condition, and predicts the magnesium oxide adhesion based on the coating condition prediction result and the magnesium oxide coating environment condition. A coating effect analysis module analyzes the magnesium oxide coating effect based on the magnesium oxide adhesion prediction result and the use data of the silicon steel use process. A coating thickness analysis module analyzes the coating thickness of the silicon steel surface based on the magnesium oxide coating effect analysis result. A coating determination module coats the silicon steel surface with magnesium oxide according to the coating thickness of the corresponding silicon steel surface.

8. 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 the silicon steel surface magnesium oxide coating quality control method based on data analysis according to any one of claims 1-6 by calling the computer program stored in the memory.

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