A method for real-time monitoring and adjustment of the surface spraying of nanocrystalline magnetic cores

Abstract

The present invention discloses a method for real-time monitoring and adjustment of the surface spraying of nanocrystalline magnetic cores, which relates to the technology of nanomaterial processing. During the spraying process, a vision sensor collects images of the spraying area in real time, and a convolutional neural network (CNN) processing algorithm is used to identify and track the thickness of the sprayed coating, generating a thickness distribution map. Combining the positional relationship between the spraying device and the vision sensor, the deviation of the sprayed coating thickness from the preset standard is calculated, and an adaptive optimization algorithm is used to adjust the spraying parameters of the spray gun, including the spraying angle, pressure, and speed, to ensure uniform thickness of the sprayed coating. By real-time monitoring and adjusting the spraying parameters, the present invention significantly improves the spraying uniformity, reduces material waste, and enhances production efficiency and product quality.

Description

Technical Field

[0001] The present invention relates to the technical field of nanomaterial processing, and particularly to a method for real-time monitoring and adjustment of the surface spraying of nanocrystalline magnetic cores. Background Art

[0002] Nanocrystalline magnetic cores have excellent magnetic properties, such as high magnetic permeability, low loss, and high saturation magnetic induction intensity, and are widely used in electronic and electrical equipment. However, existing surface treatment technologies for nanocrystalline magnetic cores have problems such as uneven powder adhesion, poor spraying uniformity, insufficient electrostatic effect, and poor control of the curing process, which affect the performance and service life of the magnetic cores.

[0003] In existing surface powder spraying devices for nanocrystalline magnetic cores, during the spraying operation, the thickness of the sprayed coating is uneven during the spraying process due to the characteristics of electrostatic spraying, uneven powder particle sizes, and the complexity of the spraying path. This non-uniformity not only affects the performance and consistency of the coating but also reduces the overall quality and reliability of the product. Therefore, it is crucial to design a method that can measure the thickness of the sprayed coating in real-time during the spraying process and make adjustments. Summary of the Invention

[0004] To address the above problems, the present invention provides a method for real-time monitoring and adjustment of the surface spraying of nanocrystalline magnetic cores. A black mark is placed on the side of the spray gun of the spraying device. During the spraying process, images of the spraying area are collected by a high-precision vision sensor placed at the end of the spraying device, and the thickness of the sprayed coating is identified and tracked in real-time using vision processing and recognition algorithms, and the distribution of the thickness of the sprayed coating is obtained by real-time solution. Combining the positional relationship between the spraying device and the vision sensor, the deviation of the thickness of the sprayed coating from the preset standard is solved. According to the deviation of the thickness of the sprayed coating, the adjustment values of the spray gun parameters are obtained using an adaptive optimization algorithm. These adjustment values are input as compensation signals in real-time to automatically adjust the spraying parameters of the spray gun, such as the spraying angle, pressure, and speed, to ensure uniform thickness of the sprayed coating. This method can not only improve the spraying uniformity but also reduce material waste, improve production efficiency, and enhance product quality.

[0005] To achieve the above invention objective, the present invention adopts the following technical solutions:

[0006] A method for real-time monitoring and adjustment of the surface spraying of nanocrystalline magnetic cores provided by the present invention specifically includes the following steps:

[0007] Step 1 Installation and calibration of equipment: Install a high-precision vision sensor at the end of the spraying device, install a powder supply system, an electric control system, and an exhaust gas treatment system, ensure the fixed position between the vision sensor and the spray gun, and perform preliminary calibration, and record their relative positional relationship;

[0008] Step 2 Pretreatment of the spraying material: Load the nano-powder material into the powder storage tank, check the particle size and distribution of the powder to ensure it meets the spraying requirements, clean and dry the surface of the nanocrystalline magnetic core to remove surface impurities and moisture, and enhance the powder adhesion;

[0009] Step 3 Real-time monitoring during spraying: During the spraying process, the vision sensor collects images of the spraying area in real time to obtain real-time image data of the nanocrystalline magnetic core, preprocesses the images using vision processing methods to form preprocessed images; Input the preprocessed images into the trained convolutional neural network (CNN) to identify the spray coating thickness distribution map, calculate the coating thickness of each pixel point in the spraying area, and form the thickness distribution map;

[0010] Step 4 Solving the thickness distribution: According to the vision processing algorithm, calculate the coating thickness distribution of each pixel point in the spraying area, and compare the thickness distribution map with the preset standard thickness to solve the thickness deviation of each area;

[0011] Step 5 Optimization of spraying parameters: According to the results of solving the thickness distribution, use the adaptive optimization algorithm to analyze the thickness deviation and obtain the adjustment value of the spray gun parameters;

[0012] Step 6 Real-time compensation of parameters: Input the adjustment value as a compensation signal into the control system in real time to automatically adjust the spraying angle, pressure and speed of the spray gun to ensure the uniformity of the spray coating thickness;

[0013] Step 7 Post-treatment after spraying: Put the sprayed nanocrystalline magnetic core into the drying equipment and carry out curing treatment at a certain temperature to make the powder coating firmly adhere to the surface of the magnetic core; After curing, cool the nanocrystalline magnetic core to room temperature and use high-precision measuring equipment to conduct surface quality inspection to ensure that the spray coating is uniform and defect-free;

[0014] Step 8 Acquisition and processing of spraying data: Record all data during the spraying process, including thickness distribution, parameter adjustment values and detection results; Analyze the data and give feedback to optimize the spraying process and equipment parameters to ensure uniformity and quality during future spraying processes.

[0015] Furthermore, among them, the vision processing algorithm described in Step 4 includes the Canny edge detection algorithm and the Otsu threshold segmentation method; The Canny edge detection algorithm detects through Gaussian filtering and double thresholds; The Otsu threshold segmentation method can separate the spray coating area from the background by automatically selecting the optimal threshold and generate a clear binary image.

[0016] Furthermore, use the Canny edge detection algorithm to extract the edge information of the spray coating;

[0017] Using the Otsu threshold segmentation method, the sprayed coating area is separated from the background.

[0018] Further, in step 3, according to the thickness distribution map output by the CNN, the coating thickness of each pixel point is calculated to form an average thickness distribution map.

[0019] Further, in step 6, the adjustment value is input into the control system as a compensation signal in real time to automatically adjust the spraying angle, pressure and speed of the spray gun to ensure uniform thickness of the sprayed coating.

[0020] Further, the adaptive optimization algorithm described in step 5 includes the gradient descent algorithm, which is used to calculate the adjustment value of the spray gun parameters; the gradient descent algorithm updates the spray gun parameters iteratively to minimize the loss function of the spraying thickness deviation.

[0021] Compared with the prior art, the above one or more technical solutions have the following beneficial effects:

[0022] (1) The method for real-time monitoring and adjustment of the surface spraying of a nanocrystalline magnetic core proposed by the present invention, by installing a high-precision vision sensor and adopting a convolutional neural network (CNN) processing algorithm, monitors the change of the coating thickness during the spraying process in real time. Using the artificial intelligence algorithm to automatically adjust the spray gun parameters makes the coating thickness more uniform, thus significantly improving the product quality.

[0023] (2) The method for real-time monitoring and adjustment of the surface spraying of a nanocrystalline magnetic core proposed by the present invention, the real-time monitoring and adaptive adjustment technology can accurately control the thickness of the sprayed coating, avoiding the common overspray and underspray problems in the traditional spraying method. This not only improves the material utilization rate, reduces the waste of powder materials, but also reduces the production cost and improves the economic benefit.

[0024] (3) The method for real-time monitoring and adjustment of the surface spraying of a nanocrystalline magnetic core proposed by the present invention, through the real-time monitoring and automatic adjustment of the spraying process, can greatly reduce the manual intervention and debugging time and improve the production efficiency. In addition, accurately controlling the spraying parameters helps to extend the service life of the equipment and reduce the maintenance cost.

[0025] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present invention. Description of the Drawings

[0026] Figure 1 is the flowchart of the method for real-time monitoring and adjustment of the surface spraying of a nanocrystalline magnetic core mentioned in the present invention;

[0027] Figure 2 is the powder spraying device for the surface of a nanocrystalline magnetic core mentioned in the present invention;

[0028] Figure 3 This is the schematic diagram of the self - adaptive powder spraying technology on the surface of the nanocrystalline magnetic core mentioned in the present invention; Detailed implementation manners

[0029] To better understand the present invention, the content of the present invention will be further clarified below in conjunction with the drawings and embodiments. However, the content of the present invention is not limited to the following scenarios only. The present invention will be further described below with reference to the drawings.

[0030] Please refer to Figure 1 , which shows the flowchart of a method for real - time monitoring and adjustment of spraying on the surface of a nanocrystalline magnetic core provided by the present invention. The specific implementation of the method is as follows:

[0031] Step 1: Installation and calibration of equipment

[0032] Among them, as Figure 2 shown, a high - precision vision sensor is installed at the end of the spraying device (i.e., the spray gun) to ensure that the entire spraying area can be recognized.

[0033] Furthermore, ensure that the position between the vision sensor and the spray gun is fixed, and conduct preliminary calibration, and record their relative position relationship.

[0034] It should be noted that except for the vision sensor and the spray gun, the installation of the powder supply system, the electronic control system and the waste gas treatment system is the same as that of the common powder spraying devices on the market.

[0035] Step 2: Pretreatment of spraying materials

[0036] Specifically, load the nano - powder material into the powder storage tank.

[0037] Furthermore, check the particle size and distribution of the powder to ensure that it meets the spraying requirements.

[0038] Furthermore, clean and dry the surface of the nanocrystalline magnetic core to remove surface impurities and moisture, and enhance the powder adhesion.

[0039] Among them, place the nanocrystalline magnetic core in an ultrasonic cleaning tank, clean it with deionized water and a neutral cleaning agent for 15 minutes, and then conduct secondary ultrasonic cleaning with ethanol for 10 minutes. After rinsing, remove oxides with a dilute acid solution, then rinse with deionized water until neutral, dry it in a hot air drying oven for 1 - 2 hours, and then conduct vacuum drying for 2 - 3 hours. Finally, conduct plasma surface activation treatment for 5 - 10 minutes to enhance the powder adhesion.

[0040] Step 2: Real - time monitoring during the spraying process

[0041] Preferably, during the spraying process, a vision sensor is required to collect the images of the spraying area in real time and obtain the real-time image data of the nanocrystalline magnetic core.

[0042] Among them, for the images of the spraying area collected by the vision sensor in real time, grayscale conversion and Gaussian blur processing are required to reduce noise interference and obtain the images after grayscale conversion and Gaussian blur processing (defined as the first preprocessed images).

[0043] Furthermore, as Figure 3 shown, the vision processing and recognition algorithm is used to identify the thickness of the spray coating on the images after the first preprocessing, calculate the coating thickness of each pixel point in the spraying area, and generate a thickness distribution map.

[0044] Among them, the Canny edge detection algorithm is used to extract the edge information of the spraying area to further reduce the noise interference in the image. Furthermore, the Otsu threshold segmentation method is used to separate the spray coating area from the background to complete the second preprocessing of the image. Finally, the images after the second preprocessing are input into the trained convolutional neural network (CNN) to generate a spray coating thickness distribution map.

[0045] Among them, the Canny edge detection algorithm is used to extract the edge information of the spray coating, and the specific formula is as follows:

[0046] edges = Canny(blurred_image, low_threshold, high_threshold)

[0047] Among them, edges is the boundary of the image, blurred_image is the input of the preprocessed image, and low_threshold and high_threshold are the preset minimum threshold and maximum threshold for edge detection respectively.

[0048] Furthermore, the Otsu threshold segmentation method is used to separate the spray coating area from the background:

[0049] binary_image = Otsu(gray_image)

[0050] Among them, binary_image is the binary image after threshold segmentation, and gray_image is the grayscale image that needs to be threshold segmented.

[0051] Step 4: Solving the thickness distribution:

[0052] Specifically, as Figure 3As shown, according to the thickness distribution map output by the CNN, calculate the coating thickness distribution of each pixel point in the spraying area, compare the thickness distribution map with the preset standard thickness, and determine whether it is less than the specified error threshold. If it is greater than the threshold, solve the thickness deviation of each area.

[0053] If it is less than the threshold, it is determined that the spraying process in this area is completed.

[0054] Among them, the calculation formula is as follows:

[0055] X r = f(X sensor , θ, d)

[0056] Among them, X r is the coordinate of the coating thickness in the fixed camera coordinate system, X sensor is the coordinate measured by the vision sensor, θ is the spray gun angle, and d is the distance between the spray gun and the vision sensor.

[0057] Step 5 Optimization of spraying parameters:

[0058] Specifically, according to the solution result of the thickness distribution, use the adaptive optimization algorithm to analyze the thickness deviation and obtain the adjustment value of the spray gun parameters.

[0059] Among them, according to the thickness deviation of each area, select the gradient descent method as the adaptive optimization algorithm to analyze the thickness deviation and obtain the adjustment value of the spray gun parameters.

[0060] It should be noted that in the present invention, any adaptive optimization algorithm can adjust the spray gun parameters. Here, the gradient descent method is selected only as an example for illustration, and the selection of the adaptive optimization algorithm has universality.

[0061] In this embodiment, the calculation formula is as follows:

[0062] ΔP = AdaptiveBest(X r - X targret )

[0063] Among them, ΔP is the adjustment value of the spray gun parameters, and X target is the preset standard thickness coordinate.

[0064] Among them, the average thickness distribution map:

[0065] thickness = calculate_thickness(thickness_map)

[0066] Among them, thickness is the calculated average thickness distribution map of the area.

[0067] Step 6 Real-time compensation of parameters:

[0068] Among them, as Figure 3 shown, when it is determined that the thickness deviation does not meet the threshold requirement, the adjustment value is input into the control system in real time as a compensation signal, and the spraying angle, pressure, and speed of the spray gun are automatically corrected through the nozzle parameter control board to ensure the uniformity of the spray coating thickness.

[0069] Step 7 Post-treatment after spraying:

[0070] Specifically, after the main process of powder spraying is completed, the sprayed nanocrystalline magnetic core is placed in a drying device and cured at a certain temperature to make the powder coating firmly adhere to the surface of the magnetic core. After curing, the nanocrystalline magnetic core is cooled to room temperature. A high-precision measuring device is used for surface quality inspection to ensure that the spray coating is uniform and defect-free.

[0071] Step 8 Acquisition and processing of spraying data:

[0072] Specifically, all data during each spraying process are recorded, including thickness distribution, parameter adjustment values, and detection results, providing a large number of samples for the training of the convolutional neural network, thereby ensuring that the training results are more accurate. Analyze the data and give feedback to optimize the spraying process and equipment parameters to ensure uniformity and quality during future spraying processes.

[0073] Combined with the above embodiments, a method for real-time monitoring and adjustment of the surface spraying of a nanocrystalline magnetic core according to the present invention can better control the thickness of the surface powder spraying compared with the traditional powder spraying device for the surface of a nanocrystalline magnetic core, improving the yield rate of products; and a method for real-time monitoring and adjustment of the surface spraying of a nanocrystalline magnetic core according to the present invention can accurately control the thickness of the spray coating by means of real-time monitoring and adaptive adjustment technology, avoiding common overspray and underspray problems in traditional spraying methods, reducing waste of powder materials while improving material utilization rate. Moreover, a method for real-time monitoring and adjustment of the surface spraying of a nanocrystalline magnetic core proposed by the present invention can perform real-time monitoring and automatic adjustment of the spraying process, significantly reducing manual intervention and debugging time and improving production efficiency. In addition, precise control of spraying parameters helps to extend the service life of the equipment and reduce maintenance costs. Therefore, the method of using the combination of computer vision and artificial intelligence to assist the surface powder spraying of a nanocrystalline magnetic core can make the coating thickness more uniform, thus significantly improving product quality.

[0074] In summary, a method for real-time monitoring and adjustment of the surface spraying of a nanocrystalline magnetic core proposed in this paper combines computer vision and artificial intelligence to assist the surface powder spraying of a nanocrystalline magnetic core, which can make the coating thickness more uniform, thus significantly improving product quality, reducing production costs, and improving economic benefits.

[0075] In the description of this specification, the description referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0076] The above-described embodiments only represent several implementation manners of the present invention. Their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

Claims

1. A method for real-time monitoring and adjustment of surface spraying of nanocrystalline magnetic cores, characterized in that: The specific steps include: Step 1: Equipment installation and calibration: Install a high-precision visual sensor at the end of the spraying device, install the powder supply system, electronic control system and exhaust gas treatment system, ensure that the position between the visual sensor and the spray gun is fixed, and perform preliminary calibration to record their relative position relationship; Step 2 Pretreatment of spraying materials: Load the nanopowder material into the powder storage tank, check the particle size and distribution of the powder to ensure that it meets the spraying requirements, clean and dry the surface of the nanocrystalline magnetic core to remove surface impurities and moisture, and enhance the adhesion of the powder; Step 3: Real-time monitoring during the spraying process: During the spraying process, the visual sensor collects images of the spraying area in real time, obtains real-time image data of the nanocrystalline magnetic core, and pre-processes the image using a visual processing method to form a pre-processed image; The preprocessed image is input into the trained convolutional neural network (CNN) to identify the spray coating thickness distribution map, calculate the coating thickness of each pixel in the spraying area, and form a thickness distribution map; Step 4: Solving the thickness distribution: According to the visual processing algorithm, calculate the coating thickness distribution of each pixel in the spraying area, and compare the thickness distribution map with the preset standard thickness to solve the thickness deviation of each area; Step 5: Optimization of spraying parameters: Based on the thickness distribution solution, the thickness deviation is analyzed using an adaptive optimization algorithm to obtain the adjustment value of the spray gun parameters; Step 6: Real-time compensation of parameters: input the adjustment value as a compensation signal into the control system in real time to automatically adjust the spraying angle, pressure and speed of the spray gun to ensure uniform thickness of the spray layer; Step 7: Post-processing after spraying: Place the sprayed nanocrystalline magnetic core in a drying device and perform a curing treatment at a certain temperature to make the powder coating firmly adhere to the surface of the magnetic core; after curing, cool the nanocrystalline magnetic core to room temperature and use high-precision measuring equipment to perform surface quality inspection to ensure that the sprayed layer is uniform and defect-free; Step 8 Collection and processing of spraying data: Record all data during the spraying process, including thickness distribution, parameter adjustment values ​​and test results; analyze data and provide feedback to optimize the spraying process and equipment parameters to ensure uniformity and quality in future spraying processes.

2. The method for real-time monitoring and adjustment of surface spraying of nanocrystalline magnetic core according to claim 1, characterized in that: in, The visual processing algorithm described in step 4 includes the Canny edge detection algorithm and the Otsu threshold segmentation method; the Canny edge detection algorithm uses Gaussian filtering and double threshold detection; the Otsu threshold segmentation method can separate the spray layer area from the background and generate a clear binary image by automatically selecting the optimal threshold.

3. The method for real-time monitoring and adjustment of surface spraying of nanocrystalline magnetic core according to claim 2, characterized in that: Use the Canny edge detection algorithm to extract the edge information of the spray layer; The spray coating area is separated from the background using the Otsu threshold segmentation method.

4. The method for real-time monitoring and adjustment of surface spraying of nanocrystalline magnetic core according to claim 1, characterized in that: In step 3, according to the thickness distribution map output by CNN, the coating thickness of each pixel is calculated to form an average thickness distribution map.

5. The method for real-time monitoring and adjustment of surface spraying of nanocrystalline magnetic core according to claim 1, characterized in that: In step 6, the adjustment value is input into the control system in real time as a compensation signal to automatically adjust the spraying angle, pressure and speed of the spray gun to ensure uniform thickness of the spray layer.

6. The method for real-time monitoring and adjustment of nanocrystalline magnetic core surface spraying according to claim 1, characterized in that: in, The adaptive optimization algorithm described in step 5 includes a gradient descent algorithm, which is used to calculate the adjustment value of the spray gun parameters; the gradient descent algorithm iteratively updates the spray gun parameters to minimize the loss function of the spray thickness deviation.

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