Tab folding detection system based on machine vision

Abstract

The invention relates to the technical field of battery production, and discloses a tab folding detection system based on machine vision, by arranging a first machine vision module, a second machine vision module and a third machine vision module, each layer of tabs of a first tab group and a second tab group on a battery cell can be subjected to accurate detection of folding defects, and the detection accuracy is improved. According to the invention, a scientific basis is provided for timely adjustment and optimization of a production line, so that the production efficiency is improved, the cost and time of manual detection are reduced, and more importantly, the quality control level of battery products is greatly improved, and the safety of the products is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of battery production, and in particular to a tab folding detection system based on machine vision. Background Art

[0002] During the production process of battery cells, it is crucial to inspect the various layers of the tabs on the tab group on the battery cell from the side to confirm whether the tabs on each layer are folded. This inspection process is not only related to the stability and performance optimization of the internal electrical connection structure of the battery, but also the cornerstone for preventing potential performance degradation. If the tab folding problem is not discovered and properly handled in time, it will profoundly affect the overall electrical conduction efficiency and stability of the battery system, which may cause a significant decline in battery performance, and even directly induce serious safety hazards such as short circuits and overheating, posing a threat to the overall quality, reliability and user safety of the product that cannot be ignored.

[0003] Therefore, how to efficiently and accurately identify and detect this potential problem, namely the tab folding phenomenon, has become an indispensable part of ensuring the safety performance and excellent quality of battery products, and is also one of the technical challenges and difficulties currently faced by the battery manufacturing industry.

[0004] The above information is presented as background information only to assist with understanding the present disclosure and no determination or admission is made as to whether any of the above may be used as prior art with respect to the present disclosure. Summary of the invention

[0005] The present invention provides a tab folding detection system based on machine vision to solve the problems existing in the prior art.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A tab folding detection system based on machine vision is used to detect folding defects of tabs of each layer of a first tab group 11 and a second tab group 12 on a battery cell, comprising a first machine vision module 21, a second machine vision module 22 and a third machine vision module 23;

[0008] The first machine vision module 21 is used to collect images of the pole tabs of each layer of the first pole tab group 11 from a first side surface of the first pole tab group 11, where the first side surface of the first pole tab group 11 is a side surface of the first pole tab group 11 away from the pole piece body 13; and collect images of the pole tabs of each layer of the second pole tab group 12 from a first side surface of the second pole tab group 12, where the second side surface of the second pole tab group 12 is a side surface of the second pole tab group 12 away from the pole piece body 13;

[0009] The second machine vision module 22 is used to collect images of the tabs of each layer of the first tab group 11 from the second side of the first tab group 11, and the second side of the first tab group 11 is symmetrically arranged with the third side of the first tab group 11 and is perpendicular to the first side of the first tab group 11;

[0010] The third machine vision module 23 is used to collect images of the tabs of each layer of the second tab group 12 from the third side of the second tab group 12, and the third side of the second tab group 12 is symmetrically arranged with the second side of the second tab group 12 and perpendicular to the first side of the second tab group 12;

[0011] The third side surface of the first electrode tab group 11 is disposed opposite to the second side surface of the second electrode tab group 12 .

[0012] Furthermore, in the tab folding detection system based on machine vision, the first machine vision module 21 includes a second camera 211, a second lens 212 and a fourth light source 213;

[0013] The second lens 212 is connected to the second camera 211;

[0014] The second camera 211 and the fourth light source 213 are respectively directed toward surfaces of the first side surfaces of the first electrode tab group 11 and the second electrode tab group 12 .

[0015] Furthermore, in the tab folding detection system based on machine vision, the second camera 211 is an area array camera;

[0016] The second lens 212 is a fixed-focus lens;

[0017] The fourth light source 213 is an annular shadowless light source.

[0018] Furthermore, in the tab folding detection system based on machine vision, the second lens 212 is a liquid fixed-focus lens.

[0019] Furthermore, in the tab folding detection system based on machine vision, the second machine vision module 22 and the third machine vision module 23 each include a first camera 221, a first lens 222, a first light source 223, a second light source 224 and a third light source 225;

[0020] The first lens 222 is connected to the first camera 221;

[0021] The first light source 223, the second light source 224 and the third light source 225 are arranged in sequence from close to the tab group to far away from the tab group;

[0022] The first camera 221 , the first light source 223 , the second light source 224 and the third light source 225 are respectively directed toward the surface of the tab group.

[0023] Furthermore, in the tab folding detection system based on machine vision, the second camera 211 is an area array camera;

[0024] The first lens 222 is a fixed-focus lens;

[0025] The first light source 223 , the second light source 224 and the third light source 225 are ring-shaped light sources.

[0026] Furthermore, in the tab folding detection system based on machine vision, the first lens 222 is a liquid fixed-focus lens.

[0027] Furthermore, the tab folding detection system based on machine vision further includes a prism 24;

[0028] The prism 24 is located between the third side surface of the first tab group 11 and the second side surface of the second tab group 12 , and is used to project light from the third side surface of the first tab group 11 and the second side surface of the second tab group 12 to the first machine vision module 21 for imaging.

[0029] Furthermore, the tab folding detection system based on machine vision further includes a light isolation sheet 25;

[0030] The light isolation sheet 25 is located between the third side surface of the first tab group 11 and the second side surface of the second tab group 12 , and is used to isolate the light transmitted by the second machine vision module 22 and the light transmitted by the third machine vision module 23 .

[0031] Furthermore, the tab folding detection system based on machine vision further includes an image processor;

[0032] The image processor is communicatively connected with the first machine vision module 21 , the second machine vision module 22 and the third machine vision module 23 , and is used to identify whether the tab is folded based on images collected by the first machine vision module 21 , the second machine vision module 22 and the third machine vision module 23 .

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The present invention provides a machine vision-based tab folding detection system, which can accurately detect folding defects of each layer of tabs of a first tab group and a second tab group on a battery cell by setting a first machine vision module, a second machine vision module and a third machine vision module, respectively, thereby providing a scientific basis for timely adjustment and optimization of the production line, thereby not only improving production efficiency and reducing the cost and time of manual inspection, but more importantly, it greatly improves the quality control level of battery products and ensures product safety.

[0035] The present invention has other features and advantages, which will be apparent from or will be described in detail in the accompanying drawings and the following detailed description incorporated herein, which together serve to explain the specific principles of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 Schematic diagram of the structure of the battery cell;

[0038] Figure 2 It is a structural schematic diagram of a tab folding detection system based on machine vision provided by an embodiment of the present invention;

[0039] Figure 3 is a structural schematic diagram of a first machine vision module provided by an embodiment of the present invention;

[0040] Figure 4 It is a schematic diagram of the structure of the second machine vision module and the third machine vision module provided in an embodiment of the present invention.

[0041] Reference numerals:

[0042] A first pole lug group 11, a second pole lug group 12, and a pole piece body 13;

[0043] A first machine vision module 21, a second machine vision module 22, a third machine vision module 23, a prism 24, and a light isolation sheet 25;

[0044] A second camera 211, a second lens 212, and a fourth light source 213;

[0045] A first camera 221 , a first lens 222 , a first light source 223 , a second light source 224 , and a third light source 225 . DETAILED DESCRIPTION

[0046] In order to explain in detail the possible application scenarios, technical principles, specific schemes that can be implemented, and the purposes and effects that can be achieved, the following is a detailed description of the specific embodiments listed in conjunction with the accompanying drawings. The embodiments described herein are only used to more clearly illustrate the technical solutions of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0047] Reference to "embodiment" herein means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present application. The term "embodiment" appearing in various places in the specification does not necessarily refer to the same embodiment, nor does it particularly limit its independence or association with other embodiments. In principle, in the present application, as long as there is no technical contradiction or conflict, the various technical features mentioned in the embodiments can be combined in any way to form a corresponding implementable technical solution.

[0048] Unless otherwise defined, the technical terms used in this document have the same meanings as those generally understood by those skilled in the art to which this application belongs; the use of relevant terms in this document is only for describing specific embodiments and is not intended to limit this application.

[0049] In the description of this application, the term "and / or" is an expression used to describe the logical relationship between objects, indicating that three relationships may exist, for example, A and / or B, which means: A exists, B exists, and A and B exist at the same time. In addition, the character " / " in this article generally indicates that the objects before and after are in an "or" logical relationship.

[0050] In the present application, terms such as “first” and “second” are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship of quantity, priority or sequence between these entities or operations.

[0051] Without further limitations, in this application, the words "include", "comprises", "has" or other similar expressions used in the sentences are intended to cover non-exclusive inclusion. These expressions do not exclude the presence of additional elements in the process, method or product including the elements, so that the process, method or product including a series of elements may include not only those limited elements, but also other elements not explicitly listed, or also include elements inherent to such process, method or product.

[0052] In this application, expressions such as "greater than", "less than", "exceed" and the like are understood to exclude the number itself; expressions such as "above", "below", "within" and the like are understood to include the number itself. In addition, in the description of the embodiments of this application, "multiple" means more than two (including two), and similar expressions related to "multiple" are also understood in this way, such as "multiple groups", "multiple times", etc., unless otherwise clearly and specifically limited.

[0053] In the description of the embodiments of the present application, space-related expressions used, such as "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "vertical", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicate the orientation or position relationship based on the orientation or position relationship shown in the specific embodiments or drawings, and are only for the convenience of describing the specific embodiments of the present application or facilitating the reader's understanding, and do not indicate or imply that the referred device or component must have a specific position, a specific orientation, or be constructed or operated in a specific orientation, and therefore cannot be understood as a limitation on the embodiments of the present application.

[0054] Unless otherwise expressly specified or limited, in the description of the embodiments of the present application, the terms such as "install", "connect", "connect", "fix", "set", etc. used should be understood in a broad sense. For example, the "connection" can be a fixed connection, a detachable connection, or an integrated setting; it can be a mechanical connection, an electrical connection, or a communication connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium; it can be the internal connection of two elements or the interaction relationship between two elements. For technicians in the technical field to which the present application belongs, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0055] Embodiment 1

[0056] In view of the above-mentioned defects in the prior art, the applicant, based on many years of rich practical experience and professional knowledge in design and manufacturing in this field, and in conjunction with the application of theory, actively conducts research and innovation in the hope of creating a technology that can solve the defects in the prior art. After continuous research and design, and after repeated trial production and improvement, the present invention with real practical value was finally created.

[0057] Please refer to Figure 1 , Figure 1 It is a schematic diagram of the structure of a battery cell, which includes a pole piece body 13, a first pole tab group 11 and a second pole tab group 12. The first pole tab group 11 and the second pole tab group 12 are both formed by multiple layers of pole tabs.

[0058] Please refer to Figure 2-4The embodiment of the present invention provides a tab folding detection system based on machine vision, which is used to detect the folding defects of the tabs of each layer of the first tab group 11 and the second tab group 12 on the battery cell, including a first machine vision module 21, a second machine vision module 22 and a third machine vision module 23;

[0059] Please refer again Figure 2 The first side surface, the second side surface and the third side surface of the first electrode tab group 11 are respectively sides A, B and C, and the first side surface, the second side surface and the third side surface of the second electrode tab group 12 are respectively sides a, b and c.

[0060] The first machine vision module 21 is used to collect images of the pole tabs of each layer of the first pole tab group 11 from the first side of the first pole tab group 11, where the first side of the first pole tab group 11 is the side of the first pole tab group 11 away from the pole piece body 13 (i.e., side A); and collect images of the pole tabs of each layer of the second pole tab group 12 from the first side of the second pole tab group 12, where the second side of the second pole tab group 12 is the side of the second pole tab group 12 away from the pole piece body 13 (i.e., side a);

[0061] The second machine vision module 22 is used to collect images of the tabs of each layer of the first tab group 11 from the second side surface of the first tab group 11, and the second side surface (i.e., side B) of the first tab group 11 is symmetrically arranged with the third side surface (i.e., side C) of the first tab group 11, and is perpendicular to the first side surface (i.e., side A) of the first tab group 11;

[0062] The third machine vision module 23 is used to collect images of the tabs of each layer of the second tab group 12 from the third side surface of the second tab group 12, and the third side surface (i.e., c side) of the second tab group 12 is symmetrically arranged with the second side surface (i.e., b side) of the second tab group 12, and is perpendicular to the first side surface (i.e., a side) of the second tab group 12;

[0063] The third side surface (ie, C surface) of the first electrode tab group 11 is disposed opposite to the second side surface (ie, b surface) of the second electrode tab group 12 .

[0064] In summary, the tab folding detection system based on machine vision provided by the present invention realizes all-round and high-precision detection of the tab folding state of the battery cell through a precisely arranged multi-view machine vision module. This innovation not only greatly improves the automation and intelligence level of the production line, reduces the dependence on manual inspection, but also plays an immeasurable role in ensuring product quality, improving production efficiency and enhancing product safety.

[0065] Please refer to it again Figure 3, to elaborate on a specific implementation of the first machine vision module 21 in this embodiment. In this configuration, the first machine vision module 21, as a core detection unit, integrates multiple key components to ensure efficient and accurate image acquisition and processing.

[0066] Specifically, the first machine vision module 21 is carefully composed of the second camera 211, the second lens 212 and the fourth light source 213. The coordinated work between the three provides a solid foundation for the detection process.

[0067] Second camera 211: As the core component of image capture, the second camera 211 is responsible for converting optical signals into electrical signals, thereby generating high-quality digital images. Its performance directly determines the clarity and accuracy of the detection results. In this embodiment, the second camera 211 is precisely installed and calibrated to ensure that it can accurately align with and capture the tab images on the first side surfaces (i.e., the A side and the a side) of the first tab group 11 and the second tab group 12.

[0068] Second lens 212: As a bridge between the camera and the detection object, the second lens 212 has a crucial impact on the image quality. It is selected and configured according to the performance parameters of the camera and the detection requirements to ensure that key indicators such as the image magnification, field of view, and distortion control meet the detection requirements. In this embodiment, the second lens 212 is closely connected to the second camera 211, and together they constitute the core unit of image acquisition.

[0069] Fourth light source 213: The light source plays a vital role in the machine vision system, which directly affects the contrast and clarity of the image. The fourth light source 213 is carefully designed and arranged to provide stable and uniform lighting conditions to ensure that the tab features on the first side of the first tab group 11 and the second tab group 12 can be clearly captured by the camera. In this embodiment, the fourth light source 213 works in conjunction with the second camera 211 to emit light toward the first side of the two tab groups, respectively, to provide necessary lighting support for the detection process.

[0070] In summary, through the precise combination and coordinated work of the second camera 211, the second lens 212 and the fourth light source 213, the first machine vision module 21 can achieve high-precision detection of the folding state of the first side tabs of the first tab group 11 and the second tab group 12 on the battery cell. This design not only improves the detection efficiency and accuracy, but also provides reliable data support for subsequent production line adjustment and optimization.

[0071] In a specific implementation of this embodiment, the following high-performance and suitable detection equipment is used for the components of the first machine vision module 21:

[0072] Second camera 211: An area array camera is selected as an image capture device. Area array cameras are known for their ability to simultaneously capture images within the entire field of view, a feature that makes them excel in application scenarios that require large-area, high-resolution images. In this system, the area array camera can capture the complete image of the first side of the first tab group 11 and the second tab group 12 at one time without scanning or splicing, thereby greatly improving the detection efficiency and accuracy.

[0073] The second lens 212 uses a fixed-focus lens as an optical element. The fixed-focus lens is known for its fixed focal length and excellent optical performance. It can provide clear and sharp images while reducing image distortion caused by focal length changes. In this system, the fixed-focus lens ensures that the second camera 211 can stably and accurately obtain images of the tab group, laying a solid foundation for subsequent image processing and analysis.

[0074] Fourth light source 213: an annular shadowless light source is selected as the lighting device. The annular shadowless light source can provide uniform, soft and shadowless lighting conditions around the object to be detected through its unique optical design. This light source can not only effectively reduce the impact of reflections and shadows on image quality, but also highlight the detailed features of the object to be detected, making defects such as folded pole ears easier to identify and judge. In this system, the annular shadowless light source provides an ideal lighting environment for the first side of the first pole ear group 11 and the second pole ear group 12, further improving the accuracy and reliability of detection.

[0075] In a specific implementation of this embodiment, a more sophisticated selection is made for the second lens 212, and a liquid fixed focus lens is used. As an advanced optical element, the liquid fixed focus lens has a series of unique advantages, making it particularly suitable for high-precision and high-requirement machine vision inspection systems.

[0076] Liquid fixed focus lenses use innovative liquid lens technology to achieve precise control and adjustment of focal length. Compared with traditional mechanical fixed focus lenses, liquid fixed focus lenses have faster response speed, higher precision and smaller size. These features enable it to quickly adapt to different inspection needs in a short time while maintaining the stability and consistency of image quality.

[0077] In this system, the application of the liquid fixed-focus lens brings multiple benefits. First, it can ensure that the second camera 211 obtains a clearer and sharper image effect when capturing the first side image of the first tab group 11 and the second tab group 12. Secondly, the rapid response and precise adjustment capabilities of the liquid fixed-focus lens enable the system to more flexibly respond to various changes that may occur on the production line, such as fine-tuning of the tab position, changes in lighting conditions, etc. Finally, due to its small size and light weight, the liquid fixed-focus lens also helps to reduce the size and weight of the entire machine vision module and improve the system's integration and portability.

[0078] In summary, the use of the liquid fixed-focus lens further improves the performance and reliability of the first machine vision module 21 in this embodiment, and provides a more accurate and efficient solution for the tab folding detection of battery products.

[0079] Please refer again Figure 4 In one implementation of this embodiment, the composition and layout of the second machine vision module 22 and the third machine vision module 23 are described in detail. The two modules are similar in structure and function, and are both designed to perform detailed inspection of the tab group from different angles or positions.

[0080] Common components: The second machine vision module 22 and the third machine vision module 23 both include a first camera 221, a first lens 222, and three light sources (a first light source 223, a second light source 224, and a third light source 225). This standardized configuration ensures the consistency and replaceability of the system, and facilitates maintenance and upgrades.

[0081] Lens and camera connection: The first lens 222 is closely connected to the first camera 221, and is responsible for clearly transmitting the image of the tab group to the camera. This connection method ensures the stability and accuracy of the image, which is the key to obtaining high-quality images.

[0082] Light source layout: Three light sources (first light source 223, second light source 224 and third light source 225) are arranged in sequence from close to the tab group to far away from the tab group. This layout not only provides sufficient illumination, but also illuminates the tab group at different angles and distances to reduce shadows and reflections, thereby more comprehensively revealing the surface details and potential defects of the tab group.

[0083] Light source and camera orientation: All light sources (first light source 223, second light source 224 and third light source 225) and first camera 221 are respectively oriented toward the surface of the tab group. This layout ensures that the camera can capture the clearest image, while the light source can illuminate each part of the tab group to the greatest extent, thereby improving the accuracy and reliability of detection.

[0084] Through this design, the second machine vision module 22 and the third machine vision module 23 can perform all-round, high-precision inspection of the tab group from different angles and positions. They work together to form a complete inspection system that can effectively identify defects such as folding, dislocation, and breakage of the tab group, providing strong support for the quality control of battery products.

[0085] In the further refinement of this embodiment, more specific selection and optimization are performed for the key components of the machine vision module. The following is a specific implementation of the second camera 211, the first lens 222 and the light source configuration:

[0086] Selection of the second camera 211: In this embodiment, the second camera 211 is explicitly designated as an area array camera. Area array cameras are known for their ability to simultaneously capture images within the entire field of view, a feature that makes them excel in application scenarios that require large-area, high-resolution images. In this system, the area array camera can capture complete images of the first tab group 11 and the second tab group 12 at one time, providing a rich data basis for subsequent image processing and analysis.

[0087] Selection of the first lens 222: In the first embodiment, the first lens 222 is designed as a fixed-focus lens. Fixed-focus lenses are known for their fixed focal length and excellent optical performance, which can provide clear and sharp images while reducing image distortion caused by focal length changes. This helps ensure that the second machine vision module 22 and the third machine vision module 23 obtain stable and high-quality images during the inspection process.

[0088] In another embodiment, the first lens 222 is further upgraded to use a liquid fixed focus lens. The liquid fixed focus lens uses innovative liquid lens technology to achieve precise control and adjustment of the focal length, with faster response speed, higher precision and smaller size. This choice enables the system to more flexibly respond to various changes that may occur on the production line while maintaining the stability and consistency of image quality.

[0089] Light source configuration: In this embodiment, the first light source 223, the second light source 224 and the third light source 225 are all configured as ring light sources. The ring light source can provide uniform, soft and shadow-free lighting conditions around the object to be detected through its unique optical design. This light source configuration can not only effectively reduce the impact of reflections and shadows on image quality, but also highlight the detailed features of the object to be detected, making defects such as folded tabs easier to identify and judge. In this system, the ring light source provides an ideal lighting environment for the second machine vision module 22 and the third machine vision module 23, further improving the accuracy and reliability of detection.

[0090] In summary, through the careful selection and optimization of the second camera 211, the first lens 222 and the light source configuration, the machine vision inspection system in this embodiment achieves a higher level of inspection performance and reliability. These improvements not only improve production efficiency, but also reduce the cost and time of manual inspection, providing strong support for the quality control of battery products.

[0091] Please refer again Figure 3 In one implementation of this embodiment, a key component, a prism 24, is introduced to achieve indirect imaging detection of the third side surface of the first tab group 11 and the second side surface of the second tab group 12. This design cleverly solves the problem that these sides cannot be directly photographed due to space limitations or detection angle problems.

[0092] The role of Prism 24:

[0093] The prism 24 is cleverly placed between the third side of the first tab group 11 and the second side of the second tab group 12. Its main function is to project the light on these two sides into the field of view of the first machine vision module 21 along a specific path through the principle of optical reflection or refraction, thereby realizing imaging detection of these sides.

[0094] Imaging principle:

[0095] When light is emitted from the third side of the first tab group 11 or the second side of the second tab group 12, it first encounters the prism 24. The prism refracts the light through its internal optical interface, causing it to change its propagation direction and head toward the camera lens of the first machine vision module 21. The camera lens then captures the refracted light and converts it into electrical signals for further processing and analysis.

[0096] Advantages and effects:

[0097] Expanded detection range: By introducing the prism 24, the system can detect the side of the pole ear that is difficult to directly observe, thereby expanding the detection range and improving the comprehensiveness and accuracy of the detection.

[0098] Avoid space limitations: In some cases, direct imaging of the side of the tab may be limited by space due to obstruction by the battery module or other components. The use of prisms can effectively bypass these obstacles and achieve unimpeded inspection.

[0099] Improve detection efficiency: Since multiple sides can be detected without moving the battery module or adjusting the position of the detection equipment, the detection efficiency can be significantly improved and the production cycle can be shortened.

[0100] In summary, the introduction of the prism 24 is an important innovation in this embodiment, which not only solves the difficulties in actual detection, but also improves the overall performance and practicality of the system.

[0101] Please refer again Figure 4 In one implementation of this embodiment, a light isolation sheet 25 is introduced as a key component to optimize the performance of the machine vision detection system. The main function of the light isolation sheet 25 is to separate the light transmitted by the second machine vision module 22 and the third machine vision module 23, respectively, to ensure that the two modules do not interfere with each other during the detection process, thereby improving the accuracy and reliability of the detection.

[0102] Position and function of the light-isolating sheet 25:

[0103] The light-isolating sheet 25 is placed at an appropriate position between the third side of the first tab group 11 and the second side of the second tab group 12. The selection of this position needs to take into account factors such as the layout of the two machine vision modules, the detection angle, and the light propagation path. The light-isolating sheet 25 effectively blocks the light emitted from one module from entering the field of view of another module through its opaque characteristics, thereby avoiding the problem of cross-light interference.

[0104] Advantages and effects:

[0105] Improve detection accuracy: Since the light isolation sheet 25 separates the light of the second machine vision module 22 and the third machine vision module 23, each module can independently and accurately capture the image of its target area. This helps to reduce image blur and ghosting caused by light interference and improve detection accuracy.

[0106] Enhanced system stability: Cross-interference of light not only affects detection accuracy, but may also damage the internal components of the machine vision module. The use of the light barrier 25 eliminates this potential risk and enhances the stability and reliability of the system.

[0107] Optimize detection layout: In some cases, due to space limitations or detection requirements, the second machine vision module 22 and the third machine vision module 23 may need to be arranged relatively closely. The introduction of the light isolation sheet 25 makes such a close layout possible while ensuring that the detection effect is not affected.

[0108] In summary, the introduction of the light isolation sheet 25 is an important measure to optimize the performance of the machine vision inspection system. It plays an important role in separating light, improving inspection accuracy and enhancing system stability, and provides more reliable technical support for the tab folding inspection of battery products.

[0109] In the further expansion of this embodiment, an image processor is introduced as a core component. As the "brain" of the entire machine vision inspection system, the image processor is responsible for processing and analyzing the image data collected by the first machine vision module 21, the second machine vision module 22 and the third machine vision module 23 to identify whether there are defects such as tab folding.

[0110] Functions and uses of image processor:

[0111] Communication connection: A stable communication connection is established between the image processor and the three machine vision modules to ensure that the image data transmitted by each module can be received in real time and accurately. This communication connection usually adopts a high-speed and reliable data transmission protocol to ensure the efficiency and accuracy of data transmission.

[0112] Image processing: After receiving the image data, the image processor will use advanced image processing algorithms to perform image preprocessing, feature extraction, pattern recognition and other operations. These operations are designed to enhance image quality, highlight target features and reduce noise interference, providing strong support for subsequent defect identification.

[0113] Defect recognition: Based on image processing, the image processor will use the preset defect recognition algorithm to analyze and compare the processed image. By comparing the difference between the actual image and the standard image, the image processor can accurately identify defects such as tab folding, misalignment, and breakage, and give the corresponding test results.

[0114] Result output: After identifying defects, the image processor will output the inspection results to the user in a visual form. This usually includes detailed information such as the location, type, size of the defect, as well as corresponding processing suggestions or alarm signals. Users can further process or adjust the battery product based on this information.

[0115] Advantages and effects:

[0116] Improve detection accuracy: By using advanced image processing algorithms and defect recognition technology, the image processor can achieve high-precision detection of defects such as tab folding. This helps reduce missed detections and false detections, and improves detection accuracy and reliability.

[0117] Improve inspection efficiency: The image processor can process and analyze image data in real time and quickly give inspection results. This greatly shortens the inspection cycle, improves inspection efficiency, and helps speed up the operation of the production line.

[0118] Reduce labor costs: Traditional tab folding detection usually requires a lot of manual participation, which is not only time-consuming and labor-intensive, but also prone to errors. After the introduction of image processors, automated detection can be achieved, greatly reducing labor costs and time costs.

[0119] In summary, the introduction of image processors is a key step in improving the performance of the tab folding detection system based on machine vision. It improves the accuracy and efficiency of detection, reduces labor costs, and provides more reliable technical support for the quality control of battery products by realizing the automatic processing and analysis of image data.

[0120] Although the terms such as machine vision module, tab, and fold are used more frequently in this application, the possibility of using other terms is not excluded. These terms are used only to more conveniently describe and explain the essence of the present invention; interpreting them as any additional restrictions is contrary to the spirit of the present invention.

[0121] The present invention provides a machine vision-based tab folding detection system, which can accurately detect folding defects of each layer of tabs of a first tab group and a second tab group on a battery cell by setting a first machine vision module, a second machine vision module and a third machine vision module, respectively, thereby providing a scientific basis for timely adjustment and optimization of the production line, thereby not only improving production efficiency and reducing the cost and time of manual inspection, but more importantly, it greatly improves the quality control level of battery products and ensures product safety.

[0122] Finally, it should be noted that although the above embodiments have been described in the specification and drawings of this application, this does not limit the scope of patent protection of this application. All technical solutions generated by replacing or modifying equivalent structures or equivalent processes based on the essential concept of this application using the contents recorded in the specification and drawings of this application, as well as directly or indirectly implementing the technical solutions of the above embodiments in other related technical fields, are included in the scope of patent protection of this application.

Claims

1. A tab folding detection system based on machine vision, used for detecting folding defects of tabs of each layer of a first tab group (11) and a second tab group (12) on a battery cell, characterized in that: Comprising a first machine vision module (21), a second machine vision module (22) and a third machine vision module (23); The first machine vision module (21) is used to collect images of each layer of the pole lugs of the first pole lug group (11) from a first side surface of the first pole lug group (11), the first side surface of the first pole lug group (11) being a side surface of the first pole lug group (11) away from the pole piece body (13); and to collect images of each layer of the pole lugs of the second pole lug group (12) from a first side surface of the second pole lug group (12), the second side surface of the second pole lug group (12) being a side surface of the second pole lug group (12) away from the pole piece body (13); The second machine vision module (22) is used to collect images of each layer of the pole tabs of the first pole tab group (11) from a second side surface of the first pole tab group (11), the second side surface of the first pole tab group (11) being symmetrically arranged with a third side surface of the first pole tab group (11) and being perpendicular to the first side surface of the first pole tab group (11); The third machine vision module (23) is used to collect images of each layer of the pole lugs of the second pole lug group (12) from a third side surface of the second pole lug group (12), the third side surface of the second pole lug group (12) being symmetrically arranged with the second side surface of the second pole lug group (12) and being perpendicular to the first side surface of the second pole lug group (12); The third side surface of the first electrode tab group (11) is arranged opposite to the second side surface of the second electrode tab group (12).

2. The tab folding detection system based on machine vision according to claim 1, characterized in that: The first machine vision module (21) comprises a second camera (211), a second lens (212) and a fourth light source (213); The second lens (212) is connected to the second camera (211); The second camera (211) and the fourth light source (213) are respectively directed toward surfaces of the first side surfaces of the first electrode tab group (11) and the second electrode tab group (12).

3. The tab folding detection system based on machine vision according to claim 2, characterized in that: The second camera (211) is an area array camera; The second lens (212) is a fixed-focus lens; The fourth light source (213) is a ring-shaped shadowless light source.

4. The tab folding detection system based on machine vision according to claim 3 is characterized in that: The second lens (212) is a liquid fixed-focus lens.

5. The tab folding detection system based on machine vision according to claim 1, characterized in that: The second machine vision module (22) and the third machine vision module (23) both comprise a first camera (221), a first lens (222), a first light source (223), a second light source (224), and a third light source (225); The first lens (222) is connected to the first camera (221); The first light source (223), the second light source (224), and the third light source (225) are arranged in sequence along a direction from close to the pole lug group to far away from the pole lug group; The first camera (221), the first light source (223), the second light source (224) and the third light source (225) are respectively directed toward the surface of the tab group.

6. The tab folding detection system based on machine vision according to claim 5, characterized in that: The second camera (211) is an area array camera; The first lens (222) is a fixed-focus lens; The first light source (223), the second light source (224) and the third light source (225) are annular light sources.

7. The machine vision-based tab folding detection system according to claim 6, characterized in that: The first lens (222) is a liquid fixed-focus lens.

8. The tab folding detection system based on machine vision according to claim 1, characterized in that: Also included is a prism (24); The prism (24) is located between the third side surface of the first pole tab group (11) and the second side surface of the second pole tab group (12), and is used to project light from the third side surface of the first pole tab group (11) and the second side surface of the second pole tab group (12) onto the first machine vision module (21) for imaging.

9. The tab folding detection system based on machine vision according to claim 1, characterized in that: Also includes a light-isolating sheet (25); The light isolation sheet (25) is located between the third side surface of the first electrode lug group (11) and the second side surface of the second electrode lug group (12), and is used to separate the light rays respectively transmitted by the second machine vision module (22) and the third machine vision module (23).

10. The tab folding detection system based on machine vision according to claim 1, characterized in that: Also included is an image processor; The image processor is communicatively connected with the first machine vision module (21), the second machine vision module (22) and the third machine vision module (23), and is used to identify whether the tab is folded based on images collected by the first machine vision module (21), the second machine vision module (22) and the third machine vision module (23).

Images