In the production of new energy batteries, the detection of pinholes in separators and coating defects is crucial. Traditional inspection methods have many deficiencies. WeLinkirt brings a new inspection solution to the industry with advanced AI vision technology.
User scenario: A leading new energy battery manufacturer needs to conduct quality inspections on battery separators and coatings in the key process of its battery production. As an important part of the battery, pinhole defects in the battery separator may cause serious problems such as battery short-circuits. The quality of the coating also directly affects the performance and safety of the battery. Therefore, accurate detection of pinholes in the separator and coating defects is an important requirement for the manufacturer's production line.
Pain points: In the past, the manufacturer used sampling inspection for quality control. This method has a high risk of missed inspections and cannot ensure the quality of every product. Additionally, the labor cost of sampling inspection is high, and the inspection efficiency is low, making it difficult to meet the needs of large-scale production. Moreover, traditional machine vision inspection methods have a relatively high false-alarm rate, which increases the workload and cost of re-inspection. When changing production models, the adjustment time of traditional inspection equipment is long, affecting the production rhythm. And with the continuous improvement of industry requirements for product quality and safety, the sampling inspection method can no longer meet compliance requirements.
Technical Principle
WeLinkirt uses advanced AI algorithms and imaging technology to solve the above problems. In terms of algorithms, deep learning algorithms are used to learn and train on a large number of good and defective product samples to establish an accurate defect recognition model. Deep learning algorithms have powerful feature extraction and classification capabilities, which can automatically learn the characteristics of pinholes in the separator and coating defects, thus achieving accurate detection. In terms of imaging, high-resolution industrial cameras and special lighting systems are used to clearly capture the subtle features of the separator and coating. By optimizing the lighting angle and intensity, the contrast of defects can be enhanced, making the defects more obvious and easier for the algorithm to identify.
- The advantage of deep learning algorithms is that they can handle complex image data and have high accuracy and robustness. Through continuous learning and optimization, the model can adapt to different types and degrees of defects, improving the generalization ability of detection.
- High - resolution industrial cameras can provide clear images, providing an accurate data foundation for the algorithm. Special lighting systems can highlight defect features and reduce the impact of environmental factors on the detection results.
- WeLinkirt also uses semantic false-alarm filtering technology. By analyzing and judging the semantic information of defects, false-alarm information is filtered out, improving the accuracy of detection.
WeLinkirt's Solution and Product Introduction
The DaoAI AI AOI software system and DaoAI 2D / 3D AI AOI equipment provided by WeLinkirt can effectively solve the manufacturer's inspection problems. The DaoAI AI AOI software system has powerful feature recognition capabilities. Through the visual basic model, it can achieve 0-code automatic programming in 5 minutes with one good product. At the same time, the system uses APDT positive-sample/few-sample learning technology, and only needs 1-20 good products to complete model training, greatly shortening the training time. In addition, the semantic false-alarm filtering function can effectively reduce the false-alarm rate. The self-developed 3D camera of the DaoAI 2D / 3D AI AOI equipment, combined with three-dimensional morphology reconstruction technology, can detect hidden solder joints, coplanarity, and micron-level morphology, providing higher accuracy for the detection of pinholes in the separator and coating defects.
WeLinkirt provides an efficient and accurate solution for new energy battery inspection with advanced technology and products.
Quantitative results: By adopting WeLinkirt's solution, the manufacturer has achieved 100% full-line inspection of pinholes in the separator and coating defects. The detection rate has reached 98.5%, and the missed-inspection rate has been reduced to <1.5%. The false-alarm rate has been reduced by -65%, greatly reducing the re-inspection workload. At the same time, during production model changes, the system can complete the adjustment in 5min, improving production efficiency and meeting the needs of large-scale production.