A battery pole piece burr detection method

Abstract

The application discloses a battery pole piece burr detection method, which comprises the following steps: obtaining an active layer edge image in a battery pole piece; calculating the active layer edge thickness; counting the active layer effective edge points through a segmented histogram; filling the active layer invalid edge points; extracting a metal layer edge image in the battery pole piece and detecting the metal layer region, so as to complete metal burr screening; the application uses segmented histogram statistics to quickly screen effective edges, uses low threshold and high threshold to extract the edges of the active layer and the metal layer respectively according to the difference between the optical characteristics of the active layer and the metal layer, uses segmented histogram statistics on the active layer edge to quickly screen out effective edges, calculates the region characteristics as a screening condition, and combines the active layer effective edge and the metal layer region characteristics, so that burrs can be accurately and quickly screened out, the detection efficiency is greatly improved, the algorithm has high precision, high speed and high real-time performance, and missed detection is avoided.

Description

Technical Field

[0001] This invention relates to the field of burr detection, and more specifically, to a method for detecting burrs on battery electrodes. Background Technology

[0002] Battery electrodes are typically made of carbon-coated copper or aluminum foil, with the carbon coating layer called the active layer and the copper or aluminum layer called the metal layer. During the slitting and cutting process, metal burrs are easily generated at the cut edges. These metal burrs may puncture the battery separator during use, causing a short circuit, which is a potential safety hazard. Currently, electrode burr detection is usually done manually by sampling, which has disadvantages such as high subjectivity, low efficiency, poor real-time performance, and is prone to missed detections, leading to serious consequences. This method is unsuitable for complex, high-speed industrial production environments.

[0003] In the existing patent (authorization announcement number: CN110706215A), the active layer and the metal layer are not distinguished during the detection process, which easily leads to false detections. Relying on human experience and manual screening with the aid of a microscope requires individual training for practitioners, resulting in high labor costs and disadvantages such as low efficiency and poor real-time performance. In addition, manual detection is highly subjective and prone to missed detections, leading to serious consequences. Furthermore, the high real-time and high-speed characteristics of industrial production environments also make it difficult for humans to perform the task effectively. Summary of the Invention

[0004] To address at least one of the aforementioned technical problems, this invention proposes a method for detecting burrs on battery electrodes.

[0005] The first aspect of this invention provides a method for detecting burrs on battery electrodes, comprising the following steps:

[0006] Acquire images of the active layer edges in the battery electrode;

[0007] Calculate the thickness at the edge of the active layer;

[0008] The effective edge points of the active layer are statistically analyzed using piecewise histograms.

[0009] Fill in the invalid edge points of the active layer;

[0010] Extract the edge image of the metal layer in the battery electrode and detect the metal layer area to complete the metal burr screening.

[0011] In a preferred embodiment of the present invention, obtaining an image of the edge of the active layer in the battery electrode sheet specifically includes:

[0012] Traverse the image column by column. Array D1 records the coordinates (x, y1) of the first pixel in each column that is greater than the set threshold T1; at the same time, array D2 records the coordinates (x, y2) of the last pixel in each column that is greater than the set threshold T1.

[0013] In a preferred embodiment of the present application, the calculation method of the edge thickness of the active layer is as follows:

[0014] The thickness of each column of the active layer can be obtained by subtracting the ordinate of the corresponding column of array D1 from the ordinate of the corresponding column of array D2, and the formula is as follows:

[0015] width=y2-y1

[0016] The thickness of each column is recorded in array W1.

[0017] In a preferred embodiment of the present application, the effective edge points of the active layer are counted by using a segmented histogram, and the specific method is as follows:

[0018] Array W1 is divided into N segments (N=5 in the present application) at equal intervals, and the histogram of each segment is counted respectively, and the thickness value with the highest occurrence frequency corresponds to the effective edge;

[0019] Arrays D1 and D2 are updated, the effective edge points are retained, and the ordinate of the ineffective edge points is set as an invalid point.

[0020] In a preferred embodiment of the present application, the method for filling the ineffective edge points includes a linear interpolation method or a linear fitting method.

[0021] In a preferred embodiment of the present application, the method for extracting the edge image of the metal layer in the battery pole piece is as follows:

[0022] The image is traversed by column, array D31 records the starting coordinate (x, y1) of the pixel point of the first segment greater than the set threshold T2 in each column, and array D32 records the ending coordinate (x, y2) of the pixel point of the first segment greater than the set threshold T2 in each column;

[0023] Array D41 records the starting coordinate (x, y3) of the pixel point of the last segment greater than the set threshold T2 in each column, and array D42 records the ending coordinate (x, y4) of the pixel point of the last segment greater than the set threshold T2 in each column.

[0024] In a preferred embodiment of the present application, the metal layer region detection method is as follows:

[0025] The region boundary surrounded by array D1 and array D2 is taken as a reference line;

[0026] All points in array D31 that exceed the reference line are counted, and the segment corresponding to the point that exceeds the reference line is marked as a candidate burr segment;

[0027] Similarly, all points in array D42 that exceed the reference line are counted, and the segment corresponding to the point that exceeds the reference line is also marked as a candidate burr segment;

[0028] The connected region detection is performed by blob analysis, adjacent candidate segments are merged into candidate burr regions, and features of each candidate burr region are calculated.

[0029] In a preferred embodiment of the present application, the obtained candidate burr regions are screened by preset area threshold TA, preset height threshold TH and preset width threshold TW, and when the features of the candidate burr regions are all greater than the preset thresholds, the candidate burr regions are considered as burr regions.

[0030] The above technical solution of the present application has the following advantages compared with the prior art:

[0031] The present application uses segmented histogram statistics to quickly screen effective edges, has high precision, fast speed, strong real-time performance and easy implementation, uses low threshold and high threshold to extract the edges of the active layer and the edges of the metal layer according to the different optical properties of the active layer and the metal layer, uses segmented histogram statistics for the active layer edge to quickly screen out effective edges, uses blob region detection for the metal layer edge to calculate region features as screening conditions, and combines the active layer effective edge and the metal layer region features to accurately and quickly screen out burrs, greatly improves the detection efficiency, and has high algorithm precision, fast speed and high real-time performance, and avoids missed detection. BRIEF DESCRIPTION OF DRAWINGS

[0032] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or the prior art description. Obviously, some of the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0033] Figure 1 is a battery pole piece cross-sectional view of an embodiment of the present application;

[0034] Figure 2 is a flow chart of a battery pole piece detection method of an embodiment of the present application;

[0035] Figure 3 is a battery pole piece metal layer distribution diagram in an embodiment of the present application;

[0036] Figure 4 is a battery pole piece metal layer distribution diagram in another embodiment of the present application. DETAILED DESCRIPTION

[0037] In order to more clearly illustrate the above-mentioned purposes, features and advantages of the present application, the present application will be further described in detail below in combination with specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0038] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without the specific details set forth in this description. In other instances, well-known methods have not been described in detail in order to avoid unnecessarily obscuring the present application.

[0039] Embodiment one

[0040] Referring to Figures 1-2 As shown in the drawings, the present application proposes a battery pole piece detection method, mainly divided into three aspects of active layer edge extraction and statistics, metal layer edge section extraction and region detection, burr screening, the specific steps are as follows:

[0041] 1) Active layer edge extraction and statistics

[0042] S1: Extract the edge of the active layer. Traverse the image by column, array D1 records the coordinates (x, y1) of the first pixel point greater than the set threshold T1 in each column; at the same time, array D2 records the coordinates (x, y2) of the last pixel point greater than the set threshold T1 in each column; then enter step S2;

[0043] S2: Active layer edge thickness calculation, subtract the vertical coordinates of the corresponding columns of array D1 and array D2, that is, the thickness of each column of active layer can be obtained:

[0044] width = y2 - y1

[0045] Record the thickness of each column in array W1, and then enter step S3;

[0046] S3: Segment histogram statistics effective edge point. Because the thickness consistency of the pole piece is very good, the use of histogram statistics can effectively screen out effective edge points and eliminate invalid points. In order to avoid the situation that the imaging thickness is not uniform due to the fact that the pole piece is not parallel to the imaging plane of the camera in actual use, the method of segmented histogram statistics can retain more effective edge points. Array W1 is divided into N segments (N = 5 in the present application) at equal intervals, and the histogram of each segment is counted respectively. The thickness value with the most occurrences corresponds to the effective edge. Update array D1 and array D2, that is, retain the effective edge points, and set the vertical coordinates of the invalid edge points to invalid points (y = -1 in the present application). Then enter step S4;

[0047] S4: Active layer edge invalid point filling. There are many methods for invalid point filling, such as linear interpolation method and straight line fitting method, so as to fill the invalid points of array D1 and array D2 in step S3; then enter step S5;

[0048] 2) Metal layer edge section extraction and region detection

[0049] S5: Extracting the edge of the metal layer. In order to detect the connected region, the edge of the metal layer is recorded by section. The image is traversed by column, the array D31 records the starting coordinate (x, y1) of the first section of each column whose pixel is greater than the set threshold T2, and the array D32 records the ending coordinate (x, y2) of the first section of each column whose pixel is greater than the set threshold T2; at the same time, the array D41 records the starting coordinate (x, y3) of the last section of each column whose pixel is greater than the set threshold T2, and the array D42 records the ending coordinate (x, y4) of the last section of each column whose pixel is greater than the set threshold T2; then step S6 is entered.

[0050] In step S5, the starting coordinate and the ending coordinate are defined as follows:

[0051] As shown in Figure 3 , it is assumed that A and B are the upper edge point and the lower edge point of the metal layer in the same column respectively, the coordinate of A is (xa, ya), and the coordinate of B is (xb, yb), wherein xa = xb (because A and B are located in the same column, the column coordinates x of A and B are equal), and ya ≤ yb. At the same time, the part between A and B is continuous, so A and B constitute a section of the metal layer in the column, and the coordinate of A is the starting coordinate of the section, and the coordinate of B is the ending coordinate of the section.

[0052] As shown in Figure 4 , it is assumed that A, B, C, D, E and F are the edge points of the metal layer in the same column, then AB constitutes the first section, CD constitutes the middle section, and EF constitutes the last section.

[0053] S6: Metal layer region detection. The region boundary surrounded by the array D1 and the array D2 is taken as the reference line, all the points in the array D31 beyond the reference line are counted, and the section corresponding to the points beyond the reference line is marked as the candidate burr section; similarly, all the points in the array D42 beyond the reference line are counted, and the section corresponding to the points beyond the reference line is also marked as the candidate burr section; then, for these candidate sections, the connected region detection is performed through blob analysis, the adjacent candidate sections are combined into the candidate burr region, and the area, height, width and other characteristics of each candidate burr region are calculated, and then step S7 is entered.

[0054] Metal burr screening

[0055] S7: Metal burr screening. According to the pre-set area threshold TA, height threshold TH, width threshold TW and the like, the candidate burr region obtained in step S6 is screened, when the characteristics of the candidate burr region are all greater than the pre-set threshold, it is considered that the candidate burr region is the burr region, so as to complete the metal burr screening.

[0056] In summary, the application uses segmented histogram statistics to quickly screen effective edges, has high precision, fast speed, strong real-time performance, is easy to implement, uses low and high thresholds to extract the edges of the active layer and the metal layer respectively according to the different optical properties of the active layer and the metal layer, uses segmented histogram statistics for the active layer edge to quickly screen out effective edges, uses blob region detection for the metal layer edge to calculate region features as a screening condition, and combines the active layer effective edge and the metal layer region features to accurately and quickly screen out burrs, greatly improves the detection efficiency, and has high algorithm precision, fast speed, and high real-time performance, avoids missed detection.

[0057] The technical features of the above embodiments can be combined in any manner. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not contradict, they should be considered as falling within the scope of the present disclosure.

[0058] The above description of disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the above embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the above-described embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0059] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for detecting burrs on battery electrode sheets, characterized in that, Includes the following steps: The process involves: acquiring images of the active layer edges in the battery electrode; calculating the edge thickness of the active layer; statistically analyzing the effective edge points of the active layer using a segmented histogram; filling in the ineffective edge points of the active layer; extracting images of the metal layer edges in the battery electrode and detecting the metal layer region to complete the metal burr screening. Obtain the edge image of the active layer in the battery electrode, including: traversing the image column by column, array D1 records the coordinates (x, y1) of the first pixel in each column that is greater than the set threshold T1; at the same time, array D2 records the coordinates (x, y2) of the last pixel in each column that is greater than the set threshold T1. The effective edge points of the active layer are counted by segmented histograms, including: dividing array W1 into N equally spaced segments, counting the histogram of each segment, and identifying the thickness value that appears most frequently as the effective edge; updating arrays D1 and D2, retaining the effective edge points, and setting the ordinate of the invalid edge points to invalid points; Extracting the edge image of the metal layer in the battery electrode includes: traversing the image column by column; array D31 records the starting coordinates (x, y1) of the first segment of each column where the pixel coordinates are greater than the set threshold T2; array D32 records the ending coordinates (x, y2) of the first segment of each column where the pixel coordinates are greater than the set threshold T2; array D41 records the starting coordinates (x, y3) of the last segment of each column where the pixel coordinates are greater than the set threshold T2; array D42 records the ending coordinates (x, y4) of the last segment of each column where the pixel coordinates are greater than the set threshold T2. Metal layer region detection includes: using the boundary of the region enclosed by arrays D1 and D2 as a baseline; counting all points in array D31 that exceed the baseline, and marking the segments corresponding to the points exceeding the baseline as candidate burr segments; similarly, counting all points in array D42 that exceed the baseline, and marking the segments corresponding to the points exceeding the baseline as candidate burr segments; performing connected region detection through blob analysis, merging adjacent candidate segments into candidate burr regions, and calculating the features of each candidate burr region.

2. The method for detecting burrs on battery electrode sheets according to claim 1, characterized in that, The method for calculating the edge thickness of the active layer is as follows: The thickness of the active layer in each column can be obtained by subtracting the y-coordinates of the corresponding columns in array D1 and array D2, as shown in the formula: width = y2 - y1; the thickness of each column is recorded in array W1.

3. The method for detecting burrs on battery electrodes according to claim 2, characterized in that, Methods for filling in invalid edge points include linear interpolation or line fitting.

4. The method for detecting burrs on battery electrode sheets according to claim 3, characterized in that, The candidate burr regions are filtered by setting a preset area threshold TA, a preset height threshold TH, and a preset width threshold TW. When the features of the candidate burr regions are all greater than the preset thresholds, the candidate burr regions are considered to be burr regions.

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