Electrophoretic coating water washing method and device for steel

Abstract

The application relates to the technical field of electroplating plating, in particular to a method and device for water washing of electrophoretic coating of steel materials, which specifically comprises the following steps: acquiring a gray-scale image of a leaf spring at each time after an electrophoretic coating water washing process of the leaf spring, constructing a strip residual coefficient of the leaf spring according to the shape change characteristic of a strip-shaped edge on the surface of the leaf spring in the image over time and the gray-scale distribution characteristic thereof, constructing a granular residual coefficient of the leaf spring based on the gray-scale characteristic change of granular residues on the surface of the leaf spring under illumination and the position distribution characteristic of the granular residues over time, judging the water washing completeness of the current batch of leaf springs based on the two parameters and the characteristics of film-shaped residues on the surface of the leaf springs, and solving the problem that, in the process of detecting the cleaning link of the electrophoretic coating process of the leaf spring by using a traditional method, the residues of cleaning liquid, electrophoretic paint liquid and impurities are easily confused, so that the current cleaning completeness is misjudged.

Description

Technical Field

[0001] This application relates to the field of electroplating technology, specifically to an electrophoretic coating washing method and apparatus for steel. Background Technology

[0002] Electrophoretic coating is a coating method that uses an external electric field to cause pigments and resin particles suspended in an electrophoretic solution to migrate directionally and deposit on the surface of a substrate, one of the electrodes. It is commonly used for processing various mechanical parts and metal components, forming a uniform coating on the metal surface. This effectively isolates the metal from the external environment, protecting the metal surface from oxidation and corrosion. The electrophoretic coating process typically includes pre-cleaning, degreasing, rust removal, neutralization, phosphating, passivation, electrophoretic coating, and drying. Each step is followed by a water washing process, primarily to remove residual chemicals and impurities from the previous step, ensuring the efficiency and quality of subsequent processing. The electrophoretic coating step is the most crucial part of the entire process. After electrophoretic coating, the leaf spring needs to be cleaned to ensure no electrophoretic paint residue remains before drying, ultimately forming a high-quality coating.

[0003] However, the entire electrophoretic coating process is typically an assembly line operation, with all process parameters set pre-defined. As the assembly line progresses, impurities in the chemicals used in each process step may increase. Changes in ambient temperature and humidity, as well as obstructions from the leaf spring structure, can lead to incomplete cleaning of the leaf springs after electrophoretic coating. After drying, defects such as pinholes and shrinkage cavities may appear on the leaf spring surface, affecting the production quality. Therefore, visual inspection is usually used to check the cleaned leaf springs to determine if they are completely cleaned. However, because residual electrophoretic paint or impurities often form granular or streaky patterns on the leaf spring surface when not completely cleaned, similar to the residue or flow of cleaning solution or pure water, traditional visual inspection methods can easily confuse the two, leading to inaccurate judgments about the leaf spring's cleanliness and ultimately affecting the production quality. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a water washing method and apparatus for electrophoretic coating of steel, and the specific technical solution adopted is as follows:

[0005] In a first aspect, embodiments of this application provide a water washing method for electrophoretic coating of steel, the method comprising the following steps:

[0006] Collect grayscale images of each batch of leaf springs at various times after electrophoretic coating and water washing;

[0007] The stripe edges and closed contours are distinguished based on the closed features of the edges in the images at different times; based on the feature differences between stripe edges in the grayscale images at different times, combined with the overall grayscale distribution of the stripe edges, the stripe residual coefficient of each batch of leaf springs is constructed.

[0008] The regions within each closed contour are divided into top and bottom regions. Based on the pixel grayscale difference between the top and bottom regions and the distance between the closed contours in the images at each time point, the particle residue coefficient of each batch of leaf springs is constructed. Combined with the stripe residue coefficient, the cleaning incompleteness of each batch of leaf springs is determined.

[0009] For each closed contour in the images of each batch of leaf springs at each time point, the film residual coefficient of each batch of leaf springs is constructed based on the gray level difference between the pixels in the neighborhood of the bottom pixel on the closed contour, as well as the gray level difference between the pixels inside and on the closed contour, combined with the pixel gray level difference on different closed contours.

[0010] The determination of whether each batch of leaf springs needs to undergo secondary cleaning is based on the degree of incomplete cleaning and the film residue coefficient.

[0011] In one embodiment, the stripe edge is a non-closed contour edge among all edges of the image.

[0012] In one embodiment, the process of obtaining the stripe residual coefficient is as follows:

[0013] Obtain the edge width of each stripe edge; number all stripe edges in each image according to their position;

[0014] Calculate the difference between the mean edge widths of all stripe edges in the first and last sampling images of each batch of leaf springs, and denote it as the first difference; calculate the difference between the minimum ordinate values ​​of all pixels on the stripe edges with the same number in the first and last sampling images of each batch of leaf springs, and denote it as the second difference; calculate the mean grayscale value of all pixels on all stripe edges in all sampling images of each batch of leaf springs.

[0015] The stripe residual coefficient of each batch of leaf springs is positively correlated with the first difference and the second difference, respectively, and negatively correlated with the mean gray value.

[0016] In one embodiment, the process of obtaining the edge width of each stripe edge is as follows:

[0017] For any pixel on the edge of each stripe, the total number of pixels with the same ordinate as the pixel and on its stripe edge is counted as the horizontal width of the pixel. The total number of pixels with the same ordinate as the pixel and on its stripe edge is counted as the vertical width of the pixel. The minimum of the horizontal and vertical widths of the pixel is taken as the edge width of the pixel. The average edge width of all pixels on each stripe edge is taken as the edge width of each stripe edge.

[0018] In one embodiment, the top region and the bottom region are respectively: the region formed by pixels with ordinates greater than or equal to the ordinate of the center point within the closed contour, and the region formed by pixels with ordinates less than the ordinate of the center point.

[0019] In one embodiment, the process of obtaining the particle residue coefficient is as follows:

[0020] Calculate the mean distance between any two center points of closed contours in each image, and denote it as the first distance mean; calculate the normalized value of the average of the first distance mean of all sampling time images of each batch of leaf springs, and denote it as the closed contour distribution dispersion.

[0021] If the average gray value of the pixels in the top region of each closed contour is less than the average gray value of the pixels in the bottom region, then the top brightness dominance coefficient of each closed contour is set to a preset first parameter; otherwise, the top brightness dominance coefficient of each closed contour is set to a preset second parameter; wherein, the first parameter is greater than the second parameter.

[0022] The particle residual coefficient of each batch of leaf springs is positively correlated with the mean of all first distances and all top brightness dominant coefficients of each batch of leaf springs.

[0023] In one embodiment, the cleaning incompleteness is the normalized value of the product of the particle residue coefficient and the stripe residue coefficient of each batch of leaf springs.

[0024] In one embodiment, the process of obtaining the film residual coefficient is as follows:

[0025] Calculate the average gray value of all pixels on the edge of each closed contour and the average gray value of all pixels inside the edge, and denot them as the first gray value and the second gray value, respectively; obtain the minimum value of the first gray value of all closed contours in the image at each sampling time; calculate the difference between the first gray value and the minimum value of each closed contour in the image at each sampling time, and denot it as the third difference; calculate the difference between the first gray value and the second gray value of each closed contour in the image at each sampling time, and denot it as the fourth difference;

[0026] Obtain the neighborhood of the pixel with the smallest ordinate on each closed contour, and take all pixels in the neighborhood whose ordinate is less than the ordinate of the smallest pixel as the occluded pixels of each closed contour. Record the gray mean of all occluded pixels of each closed contour as the third gray mean.

[0027] The thin film profile existence coefficient of each batch of leaf springs at each sampling time is positively correlated with all the third differences and all the fourth differences in the image of each batch of leaf springs at each sampling time, and negatively correlated with all the third grayscale mean values ​​in the image of each batch of leaf springs at each sampling time.

[0028] The sum of the film profile existence coefficients of all the leaf springs in each batch is used as the film residual coefficient of each batch of leaf springs.

[0029] In one embodiment, determining whether each batch of leaf springs needs secondary cleaning based on the degree of cleaning incompleteness and the film residue coefficient specifically involves:

[0030] The normalized value of the reciprocal of the product of the incomplete cleaning degree and the film residue coefficient of each batch of leaf springs is used as the cleaning completeness of each batch of leaf springs. If the cleaning completeness of each batch of leaf springs is less than or equal to the preset threshold, then each batch of leaf springs is cleaned a second time; otherwise, each batch of leaf springs is not cleaned a second time.

[0031] Secondly, embodiments of this application also provide an electrophoretic coating washing apparatus for steel, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0032] The embodiments of this application have at least the following beneficial effects:

[0033] This application analyzes grayscale images of leaf springs taken at various times after the electrophoretic coating washing process. Based on the changes in the shape of the striped edges on the leaf spring surface over time and their grayscale distribution characteristics, a stripe residue coefficient is constructed to determine the residue of electrophoretic paint or impurities. A particle residue coefficient is constructed based on the changes in the grayscale characteristics of granular residues on the leaf spring surface over time and under illumination, as well as the positional distribution characteristics of the granular residues, to further evaluate the residue of electrophoretic paint or impurities. Based on the characteristics of thin-film residues on the leaf spring surface, combined with the stripe residue coefficient and particle residue coefficient, the completeness of the washing process after the current batch of leaf springs is determined. This determines whether the electrophoretic coating washing device needs to be restarted, ensuring the completeness of the washing process after the electrophoretic coating for each batch of leaf springs, ensuring no residue on the leaf spring surface, and thus ensuring the quality of the dried leaf springs. This solves the problem that in the current traditional method of detecting the cleaning stage of the electrophoretic coating process for leaf springs, it is easy to confuse the cleaning fluid with the residue of electrophoretic paint and impurities, leading to misjudgment of the current cleaning completeness. Attached Figure Description

[0034] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 A flowchart illustrating the steps of a water washing method for electrophoretic coating of steel provided in one embodiment of this application;

[0036] Figure 2 This is a schematic diagram illustrating the process of obtaining the edge width of the stripe. Detailed Implementation

[0037] To further illustrate the technical means and effects adopted by this application to achieve the intended inventive purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the electrophoretic coating washing method and apparatus for steel proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0039] The following description, in conjunction with the accompanying drawings, details the specific scheme of the electrophoretic coating washing method and apparatus for steel provided in this application.

[0040] Please see Figure 1 It illustrates a flowchart of the steps of a water washing method for electrophoretic coating of steel according to an embodiment of this application, the method including the following steps:

[0041] Step S1: Collect grayscale images of each batch of leaf springs at various times after electrophoretic coating and water washing.

[0042] In the electrophoretic coating production of leaf springs, the leaf springs need to be cleaned after electrophoretic coating. This application installs a high-definition camera in the water washing spray area after the electrophoretic coating process. After each batch of leaf spring components is cleaned, multiple frames of images are continuously captured for that batch. In this embodiment, the number of consecutively captured image frames is 20, and the time interval between two adjacent frames is 0.5 seconds. In other embodiments of this application, the implementer can set the number of image frames and the time interval according to the actual situation. This obtains 20 frames of high-definition RGB images of a single batch of leaf spring components, and the RGB images are converted to grayscale images using the grayscale averaging method. The grayscale averaging method is a known technique, and the specific process will not be described in detail.

[0043] In this embodiment, the electrophoretic coating production line can process multiple batches of leaf spring components at one time. Each process can process one batch at a time, and each batch contains two leaf springs. The implementer should adjust the camera shooting range according to the actual situation. This application does not impose specific restrictions.

[0044] Step S2: Based on the closed features of the edges in the images at each time point, distinguish between stripe edges and closed contours; based on the feature differences between stripe edges in the grayscale images at different times, and combined with the overall grayscale distribution of the stripe edges, construct the stripe residual coefficient for each batch of leaf springs.

[0045] In the electrophoretic coating process of leaf springs, when the leaf spring has completed electrophoretic coating and is thoroughly washed, the surface is clean, without obvious particle streaks, the electrophoretic paint is evenly distributed, and the overall gloss is relatively uniform. However, if the washing is incomplete, residual electrophoretic paint may remain on the leaf spring surface, accumulating and appearing as particles or spots. Unevenly distributed paint may accumulate in some areas, forming streaks due to weight. Furthermore, due to uneven paint distribution or the presence of impurities, the gloss uniformity of the leaf spring surface is reduced, and pinholes may appear. After washing, the washing water will remain on the leaf spring surface for a certain period of time, forming particles or spots similar to residual electrophoretic paint or impurities. Additionally, the leaf spring itself is composed of multiple curved steel pieces stacked together. Under the illumination of the electrophoretic workshop, reflective edge streaks will form on the leaf spring surface, similar in shape to the streaks formed by the sliding of residual electrophoretic paint. This can lead to misjudgment during identification, thus requiring prior differentiation.

[0046] Specifically, regarding residual electrophoretic paint or impurities on the surface of leaf springs, the main manifestations are stripe edges and closed contours. For stripe edges, due to gravity, the electrophoretic paint will continue to slide down over time until the paint volume is insufficient. During this process, the overall width of the stripe edge will gradually decrease, while the edge length will gradually increase. Conversely, for edge stripes formed by reflection from the leaf spring surface, the overall brightness is higher, and because they are formed by reflection from the metal surface, their length and width hardly change.

[0047] To characterize the aforementioned features, for the grayscale image of the leaf spring at a single time point, the Sobel edge detection algorithm is used to obtain all edges in the image, and a contour tracking algorithm is further used to obtain all closed contours. The non-closed contour edges among all edges are then considered as stripe edges. For a single pixel on a single stripe edge, the total number of pixels with the same ordinate as that pixel and located on the stripe edge is counted as the horizontal width of the stripe edge at that pixel location. Then, the total number of pixels with the same ordinate as that pixel and located on the stripe edge is counted as the vertical width of the stripe edge at that pixel location. The minimum of the horizontal and vertical widths of that pixel is then taken as the edge width of the stripe edge at that pixel location. Finally, the average edge width of all pixels on the stripe edge is calculated as the edge width of the stripe edge. The Sobel edge detection algorithm and the contour tracking algorithm are well-known techniques, and their specific processes will not be elaborated further.

[0048] It should be noted that this application provides only one edge detection method and one closed contour detection method for grayscale images. There are many existing edge detection methods and closed contour detection methods, and implementers may also use other edge detection algorithms and closed contour detection algorithms to perform edge detection and closed contour detection of grayscale images. This application does not impose any specific restrictions.

[0049] The stripe edges in each image are numbered. Specifically, the coordinates of the largest pixel in the vertical coordinate of each stripe edge are taken as the position of each stripe edge. All stripe edges in the image are arranged in order from left to right and from top to bottom according to their positions to obtain the number of each stripe edge in the image.

[0050] Based on the above analysis, a stripe residue coefficient for each batch of leaf springs is constructed to characterize whether the stripe edges on the surface of the current batch of leaf springs are due to residual electrophoretic paint or impurities. The expression is as follows:

[0051]

[0052] In the formula, Let be the stripe residual coefficient of the i-th batch of leaf springs; , These are the average edge widths of all stripe edges in the nth and 1st sampling times images of the i-th batch of leaf springs, respectively, where n is the total number of times the i-th batch of leaf springs was image acquired; , Let J be the minimum value of the ordinate of all pixels on the edge of the j-th stripe in the n-th sampling time image and the 1-th sampling time image of the i-th batch of leaf springs, respectively, where j is the number of the j-th stripe edge in each image; and J is the minimum value of the number of stripes in all sampling time images of the i-th batch of leaf springs. Let be the average grayscale value of all pixels on all stripe edges in all sampling time images of the i-th batch of leaf springs. For the first difference, This is the second difference.

[0053] The meaning of this relationship is: the greater the difference in the average width of the stripe edge on the surface of the leaf spring in the i-th batch over time, the greater the difference in the lowest point of each edge stripe over time, and the lower the brightness of the stripe edge at all times, the more likely the stripe edge is to be caused by the residue of electrophoretic paint or impurities, that is, the degree of cleaning of the leaf spring in the i-th batch may be low.

[0054] Step S3: Divide the region within each closed contour into a top region and a bottom region; based on the pixel grayscale difference between the top region and the bottom region, and the distance between the closed contours in the images at each time point, construct the particle residue coefficient for each batch of leaf springs; and combine the stripe residue coefficient to determine the degree of incomplete cleaning for each batch of leaf springs.

[0055] Regarding granular residues, if the particles in the image are closed contours formed by residual electrophoretic paint or impurities, they typically present a raised, nearly circular shape. Since the lights in the electrophoresis workshop are usually positioned high and slanted downwards, the top of the closed contour is brighter than the bottom under this illumination. Conversely, if the particles are formed by water droplets formed from residual water after washing, the brightness distribution within the closed contour is more uniform, or the high-brightness areas are mainly concentrated at the bottom. Furthermore, regarding residual electrophoretic paint or impurities, after the leaf spring enters the washing process from the electrophoretic coating tank and undergoes rinsing, the distribution of the residue is more dispersed, while the water droplets remaining after washing are more densely distributed overall.

[0056] To represent the above features, for a single closed contour, the center point within the closed contour is obtained. Using the position of the center point's ordinate as a reference, pixels within the contour whose ordinate is greater than or equal to the center point's ordinate are designated as top pixels; pixels within the contour whose ordinate is less than the center point's ordinate are designated as bottom pixels. Furthermore, all top pixels constitute the top region of the closed contour, and all bottom pixels constitute the bottom region of the closed contour.

[0057] Furthermore, the mean Euclidean distance between the center points of any two closed contours in the images at each sampling time is calculated and denoted as the first distance mean. The normalized value of the average of the first distance mean in all sampling time images of the same batch of leaf springs is calculated and denoted as the closed contour distribution dispersion of all sampling time images of that batch of leaf springs. Specifically, for the normalized value of the average, this embodiment of the application normalizes the average values ​​of all batches using the maximum-minimum normalization method. Implementers may also use other normalization methods, and this application does not impose specific limitations.

[0058] Based on the above analysis, a particle residue coefficient for each batch of leaf springs was constructed to characterize the likelihood that the particles on the surface of the current batch of leaf springs are residues of electrophoretic paint or impurities.

[0059]

[0060]

[0061]

[0062] In the formula, Let be the particle residue coefficient of the i-th batch of leaf springs; The dominant top brightness value of the closed contour in all sampling time images of the i-th batch of leaf springs; Let be the dispersion of the closed contour distribution of the images at all sampling times for the i-th batch of leaf springs;

[0063] n is the total number of times when images are acquired for the i-th batch of leaf springs; Q is the minimum number of closed contours in all sampled images of the i-th batch of leaf springs. The top brightness dominance coefficient of the q-th closed contour in the image of the p-th sampling time of the i-th batch of leaf springs; This is the preset first parameter; This is the preset second parameter; , These are the average gray values ​​of the pixels in the top and bottom regions of the closed contour in the image at sampling time p of the i-th batch of leaf springs. and The value range of is (0, 1], and Preferably, in the embodiments of this application, and The values ​​are set to 1 and 0.5 respectively. In other embodiments of this application, the implementer may set the values ​​according to the actual situation. and The value of .

[0064] The top brightness dominance coefficient is used to characterize the brightness proportion of the top region within a single closed contour. Less than When the brightness of the top region of the closed contour is greater than that of the bottom region, the closed contour may be caused by residue. In this case, the dominant coefficient should be taken as a larger value. Greater than or equal to If the brightness of the top region of the closed contour is less than that of the bottom region, the closed contour may be caused by water droplets. In this case, the dominant coefficient should be a smaller value.

[0065] The meaning of this relationship is: the larger the dominant value of the top brightness of each closed contour in the grayscale image of the i-th batch of leaf springs at each time point, and the more discrete the distribution, the more likely the particles on the surface of the current leaf spring are particles generated by electrophoretic paint or impurity residue, that is, the degree of cleaning of the i-th batch of leaf springs may be low.

[0066] Based on the above analysis, the cleaning incompleteness of each batch of leaf springs is constructed to characterize the degree of incomplete cleaning in the current batch. The expression is:

[0067]

[0068] In the formula, The degree of incomplete cleaning of the i-th batch of leaf springs; Let be the stripe residual coefficient of the i-th batch of leaf springs; Let be the particle residue coefficient of the i-th batch of leaf springs; This is the normalization function. Where, This is denoted as the first product. In this embodiment, the first product of all batches of leaf springs is used as the input of the maximum-minimum method, and the output is the normalized value of the first product of each batch of leaf springs. Implementers may also use other methods to normalize it, and this application does not impose specific restrictions.

[0069] The meaning of this relationship is: the greater the possibility that the stripes and particles in the grayscale image of the i-th batch of leaf springs at each moment are electrophoretic paint liquid or impurity residues, the higher the degree of incomplete cleaning of the leaf springs, the worse the finished quality of the leaf springs after subsequent drying, and the more the cleaning process should be controlled and adjusted.

[0070] Step S4: For each closed contour in the image of each batch of leaf springs at each time point, based on the gray level difference between the pixels in the neighborhood of the bottom pixel on the closed contour, and the gray level difference between the pixels inside and on the closed contour, and combined with the pixel gray level difference on different closed contours, the film residual coefficient of each batch of leaf springs is constructed.

[0071] Because there is a lot of residual electrophoretic paint on the leaf spring surface, when the residue is small, it is easy to form granular residue. When the residue is large, it may produce striped residue. When the residue is relatively small and relatively concentrated, it may cause a local area of ​​the leaf spring surface to bulge. However, the bulge is relatively weak, and it is larger in area than the granular residue mentioned in step S3, and it will not form striped residue. Finally, a thin film is formed on the coated surface of the leaf spring. When the above steps are used for detection, it is still difficult to distinguish whether such residue still exists on the surface of the leaf spring. Therefore, further analysis is required.

[0072] Specifically, when a thin film-like residue appears, the area containing the residue is thicker than other areas of the leaf spring. This reduces the overall uniformity of gloss on the leaf spring surface in that area. The residue's protrusion is more pronounced, resulting in higher brightness at the edges compared to the central and peripheral areas when illuminated. Specifically, a closed contour area is larger, with higher brightness at the edges but lower brightness inside the contour. Furthermore, because the residue still forms a protrusion, it causes some occlusion at the lower edge, further reducing the brightness of the area below the residue. In other words, the area below the edge of the closed contour, in addition to the higher brightness of the contour itself, also experiences a further decrease in brightness.

[0073] To characterize the above features, for a single closed contour in the image at each sampling time, the pixel with the smallest ordinate on the closed contour is obtained, and a v-shaped curve is constructed with this pixel as the center. A pixel window of size 3 is defined, and all pixels within this window with a ordinate less than the defined pixel are considered occluded pixels. All occluded pixels constitute the occlusion region of the closed contour. The absolute values ​​of the differences between the center point of the closed contour and the minimum and maximum x-coordinate pixels within the closed contour are calculated, and the average of these two absolute values ​​is taken as the value of v. It should be noted that the implementer can set the value of v according to the actual situation; this application does not impose specific restrictions.

[0074] Based on the above analysis, a film residue coefficient is constructed for each batch of leaf springs to characterize the probability of film-like residues on the surface of the current batch of leaf springs. The expression is as follows:

[0075]

[0076]

[0077] In the formula, is the film residue coefficient of the i-th batch of leaf springs; n is the total number of times the i-th batch of leaf springs was image acquired; The thin film profile existence coefficient is given at the p-th sampling time of the i-th batch of leaf springs.

[0078] U represents the total number of closed contours in the image at the p-th sampling time of the i-th batch of leaf springs; Let be the average gray value of all pixels on the edge of the u-th closed contour in the image of the p-th sampling time of the i-th batch of leaf springs, denoted as the first average gray value; The minimum value of the first grayscale mean of all closed contours in the image at the p-th sampling time of the i-th batch of leaf springs; Let be the average gray value of all pixels within the edge of the u-th closed contour in the image of the p-th sampling time of the i-th batch of leaf springs, denoted as the second average gray value; Let be the average gray value of all pixels in the occluded region of the u-th closed contour in the image of the p-th sampling time of the i-th batch of leaf springs, denoted as the third gray value. For the third difference, This is the fourth difference.

[0079] The meaning of this relationship is: when the edge brightness of a single closed contour is higher than the brightness inside the closed contour at all times for the i-th batch of leaf springs, and the brightness of the area covered by the closed contour is smaller, it indicates that the closed contour is more likely to be a film residue formed by electrophoretic paint residue. In other words, the lower the degree of cleaning of the leaf spring, the more the cleaning process should be adjusted and controlled.

[0080] Step S5: Determine whether each batch of leaf springs needs to be cleaned a second time based on the degree of incomplete cleaning and the film residue coefficient.

[0081] Based on the above analysis, a cleaning completeness rating for each batch of leaf springs is constructed to characterize the degree of cleaning completeness of the current batch of leaf springs. The expression is as follows:

[0082]

[0083] In the formula, The degree of cleaning completeness of the i-th batch of leaf springs; The degree of incomplete cleaning of the i-th batch of leaf springs; Let be the film residual coefficient of the i-th batch of leaf springs; This is the normalization function. Where, This is denoted as the first reciprocal. In this embodiment, the first reciprocal of all batches of leaf springs is normalized using the maximum-minimum normalization method to determine the normalized value of the first reciprocal of each batch of leaf springs. Implementers may also use other methods to normalize it, and this application does not impose specific restrictions.

[0084] The meaning of this relationship is: the lower the degree of incomplete cleaning of the i-th batch of leaf springs and the lower the possibility of film residue, the higher the degree of complete cleaning of the current batch of leaf springs, and the more suitable it is for subsequent drying processes; conversely, it indicates that the current batch of leaf springs has incomplete cleaning and the cleaning process needs to be adjusted and controlled.

[0085] The electrophoretic coating system acquires grayscale image data of y batches of leaf springs that have been fully cleaned and marked. In this embodiment, y=100. The implementer can choose the appropriate value based on actual accuracy requirements and data availability; this application does not impose specific limitations. The cleaning completeness of each batch of leaf springs is calculated using the above method. Furthermore, all calculated cleaning completeness values ​​are used as input, and cross-validation is employed to output a threshold for determining the completeness of leaf spring cleaning. If the cleaning completeness of the current batch of leaf springs is less than or equal to this threshold, it indicates that the current batch of leaf springs still has a significant amount of electrophoretic paint or impurities remaining after the cleaning process, requiring further cleaning. At this time, the electrophoretic coating system sends a control signal. Upon receiving the control signal, the electrophoretic coating water washing device performs a secondary cleaning of the current batch of leaf springs, specifically executing the following operations:

[0086] 1. Move the current batch of leaf spring components back along the moving track to the first spray area in the water washing tank. Open the inlet valve of the spray pipe and draw water from the water washing tank. Open the nozzle valve on the spray pipe to rinse the leaf spring. The spraying time is 35 seconds. After the spraying is completed, close the nozzle valve on the spray pipe and close the inlet valve of the spray pipe.

[0087] 2. After the first spray zone is finished, the leaf spring enters the immersion zone along the moving track, and the immersion time is 60 seconds.

[0088] 3. After immersion washing, the leaf spring component enters the second spray zone along the moving track, while the drive module of the electrophoretic coating water washing device controls the spray assembly to move above the second spray zone.

[0089] 4. The spray assembly performs a secondary rinse on the soaked leaf springs in the second spray zone. At this time, the inlet valve of the spray pipe is opened, and water from the tank is drawn to rinse the leaf springs. The spraying time is 25 seconds. After rinsing, the process is repeated using the method described in this application. The process continues until the complete cleaning degree of a single batch of leaf springs exceeds the threshold value. At this point, the complete cleaning degree of the current leaf springs is considered high, and the next drying operation can proceed. The spraying and soaking times for each step can be set by the implementer according to actual conditions; this application does not impose specific restrictions.

[0090] The aforementioned secondary cleaning process is a well-known technology, and the specific process will not be described in detail here.

[0091] A schematic diagram illustrating the process of obtaining the edge width of the stripes is shown below. Figure 2 As shown.

[0092] Based on the same inventive concept as the above methods, this application also provides an electrophoretic coating washing apparatus for steel, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above electrophoretic coating washing methods for steel.

[0093] In summary, this application provides a water-washing method for electrophoretic coating of steel. By analyzing the grayscale images of leaf springs taken at various time points after the electrophoretic coating water-washing process, a stripe residue coefficient is constructed based on the time-varying shape and grayscale distribution characteristics of the striped edges on the leaf spring surface to determine the residue of electrophoretic paint or impurities. Furthermore, based on the time-varying grayscale characteristics of particulate residues on the leaf spring surface and under illumination, as well as the positional distribution characteristics of the particulate residues, a particulate residue coefficient is constructed to further evaluate the residue of electrophoretic paint or impurities. Based on the characteristics of thin-film residues on the surface of leaf springs, and combined with the stripe residue coefficient and particle residue coefficient, the degree of complete water washing after the current batch of leaf springs is determined. This allows for the determination of whether the electrophoretic coating water washing device needs to be restarted, thereby ensuring the completeness of water washing after the electrophoretic coating process for each batch of leaf springs, ensuring no residue on the leaf spring surface, and thus ensuring the quality of the dried leaf springs. This method solves the problem that the current traditional method of detecting the cleaning process of leaf springs in the electrophoretic coating process is prone to confusion between cleaning fluid and electrophoretic paint liquid and impurity residue, thus misjudging the current degree of cleaning completeness.

[0094] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this application. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0095] The various embodiments in this application are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0096] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A water washing method for electrophoretic coating of steel, characterized in that, The method comprises the following steps: Collecting gray scale images of each batch of plate springs at each time after water washing of electrophoretic coating; Distinguishing stripe edges and closed contours based on closed features of edges in images at each time; constructing stripe residual coefficients of each batch of plate springs based on feature differences between stripe edges in gray scale images at different times and combining overall gray scale distribution of stripe edges; Dividing top and bottom regions of each closed contour; constructing particle residual coefficients of each batch of plate springs based on pixel gray scale differences between top and bottom regions and distances between closed contours in images at each time, combining the stripe residual coefficients, and determining cleaning incompleteness of each batch of plate springs; For each closed contour in images at each time of each batch of plate springs, constructing film residual coefficients of each batch of plate springs based on gray scale differences between pixel points in a neighborhood of bottom pixel points on the closed contour and pixel gray scale differences between the closed contour and different closed contours. Determining whether each batch of plate springs needs secondary cleaning based on the cleaning incompleteness and the film residual coefficients; The cleaning incompleteness is a normalized value of a product of the particle residual coefficients and the stripe residual coefficients of each batch of plate springs. The determination of whether each batch of plate springs needs secondary cleaning based on the cleaning incompleteness and the film residual coefficients is specifically: Taking a normalized value of a reciprocal of a product of the cleaning incompleteness and the film residual coefficients of each batch of plate springs as a cleaning completeness of each batch of plate springs, if the cleaning completeness of each batch of plate springs is less than or equal to a preset threshold, performing secondary cleaning on each batch of plate springs; otherwise, not performing secondary cleaning on each batch of plate springs.

2. The steel material-oriented electrophoretic coating water washing method according to claim 1, characterized by, The stripe edges are non-closed contour edges among all edges of the images.

3. The steel material-oriented electrophoretic coating water washing method according to claim 1, characterized by, The acquisition process of the stripe residual coefficients is: Acquiring edge widths of each stripe edge; numbering all stripe edges in each image according to positions of the stripe edges; Calculating a difference between mean values of the edge widths of all stripe edges in the first and last sampling time images of each batch of plate springs, denoted as a first difference; calculating a difference between minimum values of longitudinal coordinates of all pixel points on stripe edges with the same number in the first and last sampling time images of each batch of plate springs, denoted as a second difference; calculating a mean value of gray scales of all pixel points on all stripe edges in all sampling time images of each batch of plate springs; The stripe residual coefficients of each batch of plate springs are in positive correlation with the first difference and the second difference and in negative correlation with the mean value of gray scales.

4. The steel material-oriented electrophoretic coating water washing method according to claim 3, characterized by, The acquisition process of the edge widths of each stripe edge is: For any pixel point on each stripe edge, counting a total number of pixel points with the same longitudinal coordinate as the any pixel point and on the stripe edge of the any pixel point as a horizontal width of the any pixel point, counting a total number of pixel points with the same horizontal coordinate as the any pixel point and on the stripe edge of the any pixel point as a longitudinal width of the any pixel point, taking a minimum value of the horizontal width and the longitudinal width of the any pixel point as an edge width of the any pixel point, and taking a mean value of the edge widths of all pixel points of each stripe edge as an edge width of each stripe edge.

5. The steel material-oriented electrophoretic coating water washing method according to claim 1, characterized by, The top region and the bottom region are respectively a region formed by pixel points with a vertical coordinate greater than or equal to a vertical coordinate of a center point of the closed contour and a region formed by pixel points with a vertical coordinate less than the vertical coordinate of the center point of the closed contour.

6. The steel material-oriented electrophoretic coating water washing method according to claim 1, characterized by, The process of obtaining the particle residual coefficient is: Calculate the average of distances between all center points of any two closed contours in each image, and denote the average as a first distance average; calculate a normalized value of an average of the first distance averages of images of all sampling time points of each batch of leaf springs, and denote the normalized value as a closed contour distribution dispersion; If the average of the pixel gray levels of the top region of each closed contour is less than the average of the pixel gray levels of the bottom region, set the top brightness dominant coefficient of each closed contour as a first preset parameter; Otherwise, set the top brightness dominant coefficient of each closed contour as a second preset parameter; the first parameter is greater than the second parameter; The particle residual coefficients of the batch of leaf springs are in a positive correlation with all the first distance averages and all the top brightness dominant coefficients of the batch of leaf springs.

7. The steel material-oriented electrophoretic coating water washing method according to claim 1, characterized by, The process of obtaining the film residual coefficient is: Calculate the average of the pixel gray levels of all pixel points on the edge of each closed contour and the average of the pixel gray levels of all pixel points inside the edge, and denote the averages as a first gray level average and a second gray level average respectively; obtain a minimum value of the first gray level average of all closed contours in each sampling time point image; Calculate a difference between the first gray level average and the minimum value of each closed contour in each sampling time point image, and denote the difference as a third difference; calculate a difference between the first gray level average and the second gray level average of each closed contour in each sampling time point image, and denote the difference as a fourth difference; Obtain a neighborhood of a pixel point with a minimum vertical coordinate on each closed contour, and take all pixel points with a vertical coordinate less than the vertical coordinate of the minimum pixel point in the neighborhood as occluded pixel points of each closed contour; and take the average of the gray levels of all occluded pixel points of each closed contour as a third gray level average; The film contour existence coefficients of each sampling time point of each batch of leaf springs are in a positive correlation with all the third differences and all the fourth differences in each sampling time point image of each batch of leaf springs, and are in a negative correlation with all the third gray level averages in each sampling time point image of each batch of leaf springs; Take a sum of all the film contour existence coefficients of each batch of leaf springs as the film residual coefficient of each batch of leaf springs.

8. An electrophoretic coating water washing device for steel materials, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1-7.

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