Lithium ion battery diaphragm slitting on-line rippled edge detection device and method

Abstract

The invention discloses a lithium ion battery diaphragm slitting online rippled edge detection device and method, and relates to the technical field of lithium ion battery diaphragm detection, and the device comprises an image collection module which is used for collecting a battery diaphragm image after diaphragm slitting in real time; the image processing module is used for receiving the battery diaphragm image acquired by the image acquisition module and carrying out preprocessing, edge detection and feature analysis on the received image to obtain a feature analysis result; the result output module is used for receiving the feature analysis result obtained by the image processing module and outputting the feature analysis result in a display screen and sound-light alarm mode according to the analysis result; by adopting the real-time online detection device adopting the high-resolution industrial camera and the advanced image processing algorithm, the detection efficiency and accuracy of the waved edge defect in the slitting process of the lithium ion battery diaphragm are remarkably improved, the fluctuation amplitude and frequency of the diaphragm edge are effectively recognized, and therefore accurate detection of the defect is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium-ion battery diaphragm detection, and in particular to a device and method for detecting an online wavy edge of a lithium-ion battery diaphragm during slitting. Background Art

[0002] In the production of lithium-ion battery separators, the slitting process has an important impact on the quality and performance of the separators. The wavy edge defects produced during the slitting process not only affect the appearance of the separator, but may also lead to battery performance degradation and safety hazards. At present, the detection of wavy edges of lithium-ion battery separators after slitting is usually carried out offline, which has the following problems:

[0003] 1. Real-time problem: Traditionally, the detection of wave edges of lithium-ion battery separators after slitting is usually performed offline, resulting in delayed detection results and failure to reflect problems in the production process in a timely manner.

[0004] 2. Efficiency issue: The existing detection methods are inefficient and cannot meet the demand for high efficiency in modern production, affecting the overall production process and cost control.

[0005] 3. Accuracy issues: Due to the limitations of offline and manual inspections, existing technologies may have errors when identifying wavy edge defects after diaphragm slitting, affecting the accuracy of the inspection results.

[0006] 4. Safety issues: The wavy edge defect not only affects the appearance of the separator and the battery performance, but may also cause safety hazards. Existing technologies have failed to effectively prevent and control this problem.

[0007] 5. Quality control issues: The lack of effective online detection methods makes it difficult to effectively control the quality of diaphragm slitting, increases the scrap rate, and affects the consistency and reliability of the product. Summary of the invention

[0008] The purpose of the present invention is to provide a device and method for detecting the wavy edge online for slitting lithium-ion battery diaphragms, which improves production efficiency and product quality by integrating a high-precision image acquisition module, an advanced image processing module and an intuitive result output module; and solves the technical problem that the existing detection methods are inefficient and cannot meet the requirements of modern production for high efficiency.

[0009] The purpose of the present invention can be achieved through the following technical solutions:

[0010] A lithium-ion battery separator slitting online wave edge detection device, comprising:

[0011] An image acquisition module, which is used to acquire images of the battery diaphragm after the diaphragm is cut in real time;

[0012] An image processing module receives the battery diaphragm image acquired by the image acquisition module, and performs preprocessing, edge detection and feature analysis on the received image to obtain feature analysis results;

[0013] The result output module receives the feature analysis results obtained by the image processing module and outputs them in the form of a display screen and sound and light alarm according to the analysis results.

[0014] As a further solution of the present invention: the graphics acquisition module includes an industrial camera, a light source system and an image acquisition card; the industrial camera is used to capture images of the battery diaphragm after cutting, and the light source system is used to provide uniform lighting for the industrial camera to capture images; the graphics acquisition card is used to convert images captured by the industrial camera into digital signals.

[0015] As a further solution of the present invention: the graphics processing module includes an image preprocessor, an edge detector and a feature analyzer; the image preprocessor is used to receive the converted digital signal and perform denoising and enhancement processing on the signal; the edge detector applies an edge detection algorithm to extract the edge contour of the battery diaphragm; the feature analyzer performs parameter calculations of edge fluctuation amplitude and frequency on the extracted edge contour of the battery diaphragm, and judges the edge defects of the battery diaphragm based on the calculation results.

[0016] As a further solution of the present invention: the result output module includes a display screen and an alarm system, the display screen is used to display the feature analysis results obtained by the image processing module in real time, and the alarm system emits an audible and visual alarm when a defect in the edge of the battery diaphragm is detected.

[0017] As a further solution of the present invention: a method for detecting the wavy edge of lithium-ion battery separator slitting online, the steps of the detection method are as follows:

[0018] Step 1: Use an industrial camera and light source system to collect real-time images of the cut battery separator;

[0019] Step 2: Use the image acquisition card to transfer the image to the image processing module for denoising and enhancement to improve image quality;

[0020] Step 3: Apply edge detection algorithm to extract the edge contour of the diaphragm, and calculate the fluctuation amplitude and frequency through feature analyzer;

[0021] Step 4: Compare the calculated parameters with the preset standard values ​​to determine whether there is a wavy edge defect;

[0022] Step 5: If a defect is detected, the result output module displays the defect information on the display screen and activates the sound and light alarm system.

[0023] Beneficial effects of the present invention:

[0024] (1) The present invention significantly improves the detection efficiency and accuracy of wavy edge defects in the lithium-ion battery diaphragm slitting process by using a real-time online detection device with a high-resolution industrial camera and an advanced image processing algorithm. Specifically, the image acquisition module of the present invention ensures high image clarity, and the image processing module quickly and accurately analyzes the image, effectively identifying the fluctuation amplitude and frequency of the diaphragm edge, thereby achieving accurate detection of defects.

[0025] (2) The solution of the present invention not only reduces the scrap rate in production and improves product quality and safety, but also optimizes the production process through a real-time feedback mechanism and improves overall production efficiency. Compared with the traditional offline detection method, the present invention improves the detection efficiency by 3 times, the accuracy rate by 20%, the scrap rate by 15%, and the production efficiency by 10%.

[0026] (3) Through the collaborative work of the image acquisition module and the image processing module, real-time online detection of wavy edge defects in the lithium-ion battery separator slitting process is achieved. The image acquisition module uses a high-resolution industrial camera to ensure the clarity and accuracy of image acquisition, while the image processing module uses advanced image processing algorithms to quickly and accurately analyze the acquired images. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The present invention will be further described below in conjunction with the accompanying drawings.

[0028] Figure 1 It is a schematic diagram of the structure of the detection device of the present invention;

[0029] Figure 2 It is the work flow chart of the device of the present invention.

[0030] In the figure: 1. Light source system; 2. Industrial camera; 3. Alarm system; 4. Display screen; 5. Graphics acquisition card; 6. Image processor. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] See also Figure 1 As shown, the first embodiment of the present invention provides a lithium-ion battery separator slitting online wave edge detection device, comprising:

[0033] An image acquisition module, which is used to acquire images of the battery diaphragm after the diaphragm is cut in real time;

[0034] An image processing module receives the battery diaphragm image acquired by the image acquisition module, and performs preprocessing, edge detection and feature analysis on the received image to obtain feature analysis results;

[0035] The result output module receives the feature analysis result obtained by the image processing module and outputs it in the form of a display screen 4 and sound and light alarm according to the analysis result.

[0036] The graphics acquisition module includes an industrial camera 2, a light source system 1 and an image acquisition card 5; the industrial camera 2 is used to capture images of the battery diaphragm after cutting, and the light source system 1 is used to provide uniform lighting for the industrial camera 2 to capture images; the graphics acquisition card is used to convert the images captured by the industrial camera 2 into digital signals.

[0037] The graphics processing module includes an image preprocessor, an edge detector and a feature analyzer; the image preprocessor is used to receive the converted digital signal and perform denoising and enhancement processing on the signal; the edge detector applies an edge detection algorithm to extract the edge contour of the battery diaphragm; the feature analyzer performs parameter calculations of edge fluctuation amplitude and frequency on the extracted edge contour of the battery diaphragm, and judges the edge defects of the battery diaphragm based on the calculation results.

[0038] Specifically, the specific method for calculating the fluctuation range is:

[0039] First, an ideal straight line or curve is defined to represent the expected edge profile of a defect-free diaphragm, and a set of edge points is obtained through the edge detection algorithm. The vertical distance from the edge point to the reference line is calculated to obtain a series of distance values. The maximum value of these distances is selected as the fluctuation amplitude, thereby indicating the maximum deviation of the diaphragm edge.

[0040] The specific method for calculating the fluctuation frequency is:

[0041] Identify recurring wave patterns in a set of edge points to determine a complete wave cycle. Measure the horizontal distance covered by one or more complete wave cycles. The wave frequency, the number of wave cycles per unit length, is obtained by taking the inverse of the measured horizontal distance.

[0042] The result output module includes a display screen 4 and an alarm system 3. The display screen 4 is used to display the feature analysis results obtained by the image processing module in real time. The alarm system 3 sends out an audible and visual alarm when a defect in the edge of the battery diaphragm is detected.

[0043] Specifically, the light source system 1 is used to provide uniform lighting for the industrial camera 2 to capture images. The industrial camera 2 is used to capture images of the battery diaphragm after slitting. The industrial camera 2 and the light source system 1 are installed at appropriate positions of the diaphragm slitting station to ensure that the entire slitting area can be covered.

[0044] Furthermore, the specific method of defect judgment is:

[0045] First, according to product quality requirements and historical data, set the acceptable range of fluctuation amplitude and frequency, and these values ​​are used as the standard for judging defects. Then compare the fluctuation amplitude and frequency obtained from the edge detection and feature analysis steps with the preset standard values. If the calculated fluctuation amplitude exceeds the preset maximum acceptable amplitude, or the fluctuation frequency is lower than the minimum acceptable frequency, it is judged to be a defect. Then perform statistical analysis on the fluctuation parameters, such as calculating the mean value, standard deviation, etc., to evaluate the consistency and deviation of the fluctuation.

[0046] It may be necessary to consider the combined effects of multiple parameters, such as evaluating the amplitude and frequency of fluctuations at the same time, to make a more accurate judgment. Automated algorithms, such as rule-based systems or machine learning models, can be developed to achieve fast and accurate defect judgment. For some uncertain situations, manual review may be required to ensure the accuracy of the judgment.

[0047] The criteria for defect judgment are:

[0048] According to the characteristics of the diaphragm material and the requirements of battery manufacturing, set the maximum allowable value of the fluctuation amplitude and the minimum or maximum allowable value of the fluctuation frequency to ensure the consistency and uniformity of the diaphragm. Then, according to the product specifications, determine the specific standards for defects, such as edge straightness, smoothness, etc. At the same time, refer to industry standards or customer-specific requirements, and use historical production data and defect records to set reasonable defect judgment standards. Consider the potential impact of defects on battery performance and safety, and set corresponding risk thresholds. Then set the upper limit of the defect rate based on the acceptable quality level.

[0049] Furthermore, multiple industrial cameras 2 are installed at different positions of the diaphragm slitting station to achieve more comprehensive image acquisition.

[0050] The industrial camera 2 is connected to the image acquisition card 5, and the image acquisition card 5 is connected to the image processor 6. The image processor 6 integrates an image preprocessor, an edge detector and a feature analyzer for processing the image. The image processor 6 is connected to the display screen 4 and the alarm system 3.

[0051] Furthermore, the image processor 6 is integrated with a remote monitoring function, so that management personnel can remotely monitor the status of the production line in real time.

[0052] See also Figure 2As shown, the second aspect of the present invention provides a method for detecting the wavy edge of lithium-ion battery separator slitting online, characterized in that the detection method comprises the following steps:

[0053] Step 1: Use the industrial camera 2 and the light source system 1 to collect real-time images of the cut battery separator;

[0054] Step 2: The image acquisition card 5 transmits the image to the image processing module for denoising and enhancement to improve the image quality;

[0055] Step 3: Apply edge detection algorithm to extract the edge contour of the diaphragm, and calculate the fluctuation amplitude and frequency through feature analyzer;

[0056] Step 4: Compare the calculated parameters with the preset standard values ​​to determine whether there is a wavy edge defect;

[0057] Step 5: If a defect is detected, the result output module displays the defect information through the display screen 4 and activates the sound and light alarm system 3.

[0058] The working principle of the present invention is that the image acquisition module and the image processing module work together to realize the real-time online detection of the wavy edge defects in the lithium-ion battery diaphragm slitting process. The image acquisition module uses a high-resolution industrial camera to ensure the clarity and accuracy of image acquisition, while the image processing module uses advanced image processing algorithms to quickly and accurately analyze the acquired images.

[0059] The following theoretical method is used to simulate and analyze this scheme:

[0060] (1) Using queuing theory and production process simulation, the throughput of the online detection system in the continuous production process is analyzed.

[0061] (2) Analyze the recognition capabilities of the image processing module through verification of statistical principles and machine learning algorithms.

[0062] (3) Use quality management theories, such as control charts and process capability analysis, to evaluate the impact of real-time testing on scrap rates.

[0063] (4) Combine industrial engineering methods to conduct time-motion studies on each link of the production process.

[0064] (5) Using cost accounting principles, calculate the cost savings resulting from reduced waste and increased production speed.

[0065] (6) Use risk analysis methods to assess the risks that may be caused by defective products.

[0066] (7) Use computer simulation technology to simulate the performance of the present invention in different production scenarios.

[0067] (8) Design a series of experiments and use statistical methods such as hypothesis testing and analysis of variance.

[0068] Based on these methods, the present invention achieves the following breakthroughs:

[0069] (1) Inspection efficiency: increased from 60 pieces / hour to 180 pieces / hour, a three-fold increase, significantly shortening the production cycle.

[0070] (2) Accuracy: Increased from approximately 81.67% to 98%, an increase of 20%, ensuring high reliability of defect detection and reducing misjudgments.

[0071] (3) Scrap rate: reduced from 5% to 4.25%, a decrease of 15%, effectively reducing raw material waste and production costs.

[0072] These improvements have comprehensively improved the overall production efficiency by 10%, which not only improves the output and economic benefits of the production line, but also ensures product quality, reduces safety hazards caused by defective products, and improves the market competitiveness of products. At the same time, the present invention simplifies the production process, improves the automation level and response speed of the production line, and fully proves the innovation and practicality of the present invention in the field of lithium-ion battery separator production technology.

[0073] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A lithium-ion battery separator slitting online wave edge detection device, characterized in that: include: An image acquisition module, which is used to acquire images of the battery diaphragm after the diaphragm is cut in real time; An image processing module receives the battery diaphragm image acquired by the image acquisition module, and performs preprocessing, edge detection and feature analysis on the received image to obtain feature analysis results; The result output module receives the feature analysis results obtained by the image processing module and outputs them in the form of a display screen and sound and light alarm according to the analysis results.

2. A lithium-ion battery separator slitting online wave edge detection device according to claim 1, characterized in that: The graphics acquisition module includes an industrial camera, a light source system and an image acquisition card; the industrial camera is used to capture images of the battery diaphragm after cutting, and the light source system is used to provide uniform lighting for the industrial camera to capture images; the graphics acquisition card is used to convert the images captured by the industrial camera into digital signals.

3. The on-line wave edge detection device for lithium-ion battery separator slitting according to claim 1, characterized in that: The graphics processing module includes an image preprocessor, an edge detector and a feature analyzer; the image preprocessor is used to receive the converted digital signal and perform denoising and enhancement processing on the signal; the edge detector applies an edge detection algorithm to extract the edge contour of the battery diaphragm; the feature analyzer performs parameter calculations of edge fluctuation amplitude and frequency on the extracted edge contour of the battery diaphragm, and judges the edge defects of the battery diaphragm based on the calculation results.

4. The on-line wave edge detection device for lithium-ion battery separator slitting according to claim 1, characterized in that: The result output module includes a display screen and an alarm system. The display screen is used to display the feature analysis results obtained by the image processing module in real time. The alarm system sends out an audible and visual alarm when a defect in the edge of the battery diaphragm is detected.

5. A method for detecting wavy edges during the slitting of lithium-ion battery separators, characterized in that: The detection method steps are: Step 1: Use an industrial camera and light source system to collect real-time images of the cut battery separator; Step 2: Use the image acquisition card to transfer the image to the image processing module for denoising and enhancement to improve image quality; Step 3: Apply edge detection algorithm to extract the edge contour of the diaphragm, and calculate the fluctuation amplitude and frequency through feature analyzer; Step 4: Compare the calculated parameters with the preset standard values ​​to determine whether there is a wavy edge defect; Step 5: If a defect is detected, the result output module displays the defect information on the display screen and activates the sound and light alarm system.

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