As an important safety component of lithium batteries, the quality inspection of explosion-proof valves is particularly important. Traditional inspection methods mainly rely on manual work, which is inefficient and easily influenced by human factors, resulting in misjudgment or missed inspections. Therefore, a lithium battery manufacturer hopes to introduce Intsoft Tech’s machine vision technology to achieve efficient and accurate automatic inspection.
Detection requirements and challenges
In the production process of lithium batteries, surface defect detection of explosion-proof valves has become a technical problem. The main challenges include:
1. Surface detail capture: The surface structure of the explosion-proof valve is complex, requiring the machine vision system to capture its subtle features and changes.
2. Dynamic detection requirements: On the production line, the explosion-proof valve moves at a fast speed, requiring the machine vision system to have the ability to respond quickly and process in real time.
3. Ambient lighting impact: The lighting conditions in the manufacturing workshop may be unstable, affecting the clarity and contrast of the image.
4. Surface reflection: The surface material of the explosion-proof valve may reflect light, causing some areas in the image to be too bright or too dark, affecting the accuracy of defect detection.
5. Consistency detection of different batches: It is necessary to ensure that explosion-proof valves from different batches and under different production conditions can obtain consistent and accurate detection results.
Solution
To solve the above challenges, we provide customers with an effective solution:
1. Camera selection and parameter setting
In order to obtain high-quality images, we selected a 600W high-resolution rolling shutter camera with a field of view of 60*40mm and pixel accuracy of 0.019mm/pix. This configuration ensures that every detail of the explosion-proof valve can be captured.
For surface defects such as bumps and pits on the explosion-proof valve, we use a 3D structured light camera for detection. This technology uses light stripes projected on an object to obtain the three-dimensional structural information of the object through the camera’s visual system. Its sampling interval is set to 0.03mm/pix, ensuring high-precision detection of subtle defects
2. Image processing and algorithm application
Film warping detection: When the explosion-proof valve film is warped, the part that should be covered by the film will be exposed and appear gray. At the same time, the warped part will reflect light and the brightness is higher than the normal part. By detecting changes in brightness and color, it is possible to accurately determine whether the film is warped.
Burn-through mark detection: The burn-through part will leave obvious dark spots. By detecting the existence and shape of the dark spots, it can be determined whether the explosion-proof valve is burned through.
Film deviation detection: If the film is pasted unaligned, its edge will appear uneven. By analyzing the uniformity of the edge, it can be determined whether the film is unaligned.
Film existence detection: Based on the image processing algorithm, the pixel distribution in the explosion-proof valve area is analyzed to determine whether the film exists.
In addition, we also use visual algorithms to fit the hemispherical surface of the explosion-proof valve into a plane and calculate its flatness to evaluate the integrity of the explosion-proof valve. This technology improves detection accuracy and reliability.
Solution Results
The solution met the customer’s detection requirements, was successfully implemented and achieved the following significant results:
High accuracy: For various defects such as film warping, burn-through marks, film deviation and film existence, the detection accuracy reached more than 99%.
High efficiency: Compared with traditional manual detection methods, machine vision technology greatly improves detection efficiency and reduces labor costs.
Stability: Since the machine vision system is not affected by fatigue and human factors, its detection results are more stable and reliable.
