In the production process of lithium batteries, the diaphragm is a key component, and its quality directly affects the performance and safety of the battery. However, the problem of detecting pinholes and coating defects in the diaphragm has always troubled the industry. DaoAI provides an effective solution to this problem with advanced technology and innovative solutions.
Scenario: In the complex structure of lithium-ion batteries, the diaphragm plays a crucial role. It is a key functional layer that isolates the positive and negative electrodes while allowing lithium-ions to pass through freely, thus ensuring the normal charging and discharging processes of the battery. Imagine a lithium-ion battery as a precise chemical plant, where the chemical reactions between the positive and negative electrodes need to take place in a relatively stable and safe environment, and the diaphragm is the guardian of this environment. However, once a pinhole appears on the diaphragm, even an extremely tiny one, it may become the starting point of an internal short-circuit. Once an internal short-circuit occurs, it is like triggering a small-scale 'explosion' in this precise chemical plant, which may cause the battery to overheat, catch fire, or even explode, seriously threatening the safety and performance of the battery. In addition, the quality of the coating is also of great importance. Defects such as coating scratches, missing coatings, and uneven coating thickness can affect the consistency of ion passage and thermal stability, thereby affecting the overall performance of the battery. Therefore, in the production process of the diaphragm, accurate detection of pinholes and coating defects is a key step to ensure the quality of lithium-ion batteries.
Pain Points: Why It's Difficult
In terms of speed, the diaphragm production line of a certain battery factory has a very high speed, which poses extremely high requirements for the response speed of the detection system. In the case of high-speed movement, it is very difficult for traditional visual inspection systems to capture clear and accurate images in a short time. Just like using an ordinary camera to shoot a high-speed racing car, it is hard to get a clear picture. Moreover, high-speed movement can also cause problems such as image blurring and distortion, further increasing the difficulty of detection. According to statistics, in the high-speed operation state of this production line, the imaging success rate of traditional visual inspection systems is less than 60%.
In terms of size, the defect size is often in the micron-level. A micron is a very small unit of length, with 1 micron equal to one-thousandth of a millimeter. Such tiny defects are like looking for an extremely small grain of sand in the vast sea for traditional visual inspection systems. Traditional optical devices can hardly distinguish such tiny objects, and the tiny defects occupy a very small pixel ratio in the image, making them easy to be ignored. For example, a pinhole with a diameter of 5 microns may only occupy a few pixels in an ordinary image, making it difficult to be accurately identified.
Analyzed from the contrast dimension, the diaphragm substrate of this battery factory is semi-transparent, which makes the contrast between the defect and the normal area extremely low. In this case, it is very difficult for traditional visual inspection systems to distinguish between defects and normal substrate textures. Just like there is a tiny flaw on a semi-transparent glass, due to the semi-transparent nature of the glass itself, it is hard to clearly see this flaw. Moreover, the substrate texture may also mislead the detection system, causing it to misjudge normal textures as defects, thereby increasing the false-alarm rate. According to actual production data, the false-alarm rate of traditional visual inspection on this production line is as high as 30%.
Technical Principles
The high-speed coil online AI-AOI solution deployed by DaoAI is centered around its dedicated lighting and high-speed imaging technology designed for semi-transparent diaphragms. The dedicated lighting system can provide appropriate lighting conditions according to the characteristics of the diaphragm, enabling pinholes to form stable contrast signals under transmitted or specific lighting. It's like using a special light to illuminate a tiny object in the dark to make it clearly visible. The high-speed imaging technology can quickly and accurately capture images of the diaphragm in high-speed movement. Through high-speed cameras and advanced image-processing algorithms, clear and high-quality images can be obtained in a short time, providing a reliable data basis for subsequent defect identification.
In terms of defect identification, a powerful AI model is combined. This AI model has been trained with a large number of samples and can accurately identify defects such as pinholes, coating scratches, missing coatings, and uneven coating thickness. Compared with traditional rule-based identification methods, the AI model has stronger adaptability and flexibility. Traditional methods need to manually write rules according to different defect types and characteristics. When encountering new defect types or complex situations, it is difficult to conduct effective identification. The AI model can automatically extract defect features by learning a large amount of sample data, thus achieving more accurate identification. In addition, the system also uses APDT positive-sample learning technology, which can adapt to different membrane types and coating formulations. By continuously learning new positive-sample data, the model can continuously optimize and adjust the identification strategy to adapt to different production environments and product requirements.
Typical Application Scenarios
- Pinhole detection: During the high-speed production process of the diaphragm, pinholes are extremely difficult to detect due to their tiny size and low contrast on the semi-transparent substrate. DaoAI's dedicated lighting makes pinholes form obvious dark spots under transmitted light. The high-speed imaging system quickly captures the image, and the AI model accurately identifies pinholes through the feature analysis of the dark spots in the image. The difficulty lies in how to stably image in high-speed movement and distinguish tiny pinholes from normal textures.
- Coating scratch detection: Coating scratches may be caused by friction of production equipment or other reasons. These scratches are usually very thin and not easily noticeable on the semi-transparent diaphragm. DaoAI highlights the difference between scratches and normal coatings through specific lighting, obtains clear images with high-speed imaging, and the AI model identifies scratches according to their line features. The difficulty is that the shapes and lengths of scratches vary, and the model needs to have strong adaptability.
- Missing coating detection: There are certain differences in color and texture between the missing-coating area and the normal coating area, but these differences are not obvious under the semi-transparent substrate. DaoAI's lighting system enhances this difference, the high-speed imaging records the image, and the AI model identifies the missing-coating area through comparative analysis. The difficulty lies in accurately defining the boundary of the missing-coating area to avoid misjudgment.
- Uneven coating thickness detection: The thickness change of the coating is in the micron-level, which is difficult to detect by traditional methods. DaoAI uses optical principles to reflect the difference in coating thickness through the change in the intensity of the reflected light of the coating under illumination. The high-speed imaging obtains the reflected-light image, and the AI model identifies the area with uneven thickness according to the distribution of the reflected-light intensity. The difficulty lies in how to accurately measure the tiny thickness change and distinguish normal thickness fluctuations from defects.
Implementation Case
A large-scale battery manufacturing enterprise has a large-scale diaphragm production line with high speed and wide width. Before introducing DaoAI's high-speed coil online AI-AOI solution, the enterprise used a traditional visual inspection system, with a pinhole detection rate of only 70%, a missed-detection rate as high as 30%, and a false-alarm rate of 30%, which seriously affected production efficiency and product quality. During the implementation process, DaoAI's technical team first conducted a detailed investigation and analysis of the production line and carried out customized deployment of the system according to the actual situation of the production line. After a period of debugging and optimization, the system was successfully launched. After the launch, the pinhole detection rate increased to 98%, the missed-detection rate decreased to 2%, and the false-alarm rate decreased to 5%. At the same time, the detection effect of coating defects was also significantly improved, and the consistency and traceability of the products were greatly enhanced.
DaoAI's solution has brought a qualitative leap to the diaphragm production of this enterprise, effectively solving the problems of traditional detection methods.
DaoAI's Solution and Products
DaoAI's high-speed coil online AI-AOI solution is a comprehensive solution. It includes dedicated lighting equipment, a high-speed imaging system, and an advanced AI algorithm model. The dedicated lighting equipment can provide the best lighting conditions according to different diaphragm characteristics to ensure that defects can be clearly visible. The high-speed imaging system can quickly and accurately obtain high-quality images in high-speed movement. The AI algorithm model is the core of the whole solution, which can perform real-time analysis and processing on images and accurately identify various defects. In addition, the system uses APDT positive-sample learning technology, which can quickly adapt to different membrane types and coating formulations, realize online full-inspection coverage in the width direction, and its rhythm can match the high-speed coil production line to ensure that production efficiency is not affected.
Quantitative Results
Through practical applications, DaoAI's solution has achieved significant quantitative results. In terms of pinhole detection, the detection rate has increased from the original 70% to 98%, greatly improving the safety of the products. The missed-detection rate has decreased from 30% to 2%, effectively preventing defective products from flowing into downstream processes. The false-alarm rate has decreased from 30% to 5%, reducing unnecessary manual intervention and production pauses and improving production efficiency. Similar effects have also been achieved in coating defect detection. The overall detection rate of coating defects has reached over 95%, and the consistency and traceability of the products have been greatly improved, providing double guarantees for the enterprise's production quality and economic benefits.
FAQ
What impacts do pinholes and coating defects on the diaphragm have?
Pinholes on the diaphragm can damage insulation and become the starting point of an internal short-circuit, which may cause the battery to overheat, catch fire, or even explode, seriously threatening battery safety. Defects such as coating scratches, missing coatings, and uneven coating thickness can affect the consistency of ion passage and thermal stability, thereby affecting the overall performance of the battery. DaoAI can stably detect such defects through advanced technology to ensure battery quality.
What problems does traditional visual inspection have on the diaphragm production line?
The diaphragm production line of a certain battery factory has a high speed and a wide width, and the substrate is semi-transparent, with low defect contrast and tiny defect sizes. Traditional vision has difficulty in stably imaging under high-speed movement, resulting in a low imaging success rate. It is also easily misled by the substrate texture and misjudges normal textures as defects, leading to a high false-alarm rate. DaoAI can effectively solve these problems.
What effects can DaoAI's product solution bring?
DaoAI deploys a high-speed coil online AI-AOI solution. After going live, it can achieve online full-inspection of pinholes and coating defects at high speeds. The pinhole detection rate has increased significantly to 98%, and the missed-detection rate and false-alarm rate have been significantly reduced. It can prevent defects from flowing downstream, improve the consistency and traceability of incoming materials, and enhance production efficiency and product quality.
This article was generated by AI. Customer cases are simulated scenarios based on real product capabilities and figures are illustrative; see product pages for official benchmarks.