In electronics/PCBA production, the detection of component polarity reversal is a crucial step to ensure product quality. Traditional detection methods have a high false alarm rate, and WeLinkirt's AI vision technology provides an effective solution to this problem.
User scenario: The SMT (Surface Mount Technology) production line of a leading electronics/PCBA manufacturer, which mainly produces PCBA boards for various electronic products. The detection objects are polar components on the PCBA boards, such as capacitors and diodes. The correct installation of the polarity of these components directly affects the performance and stability of the products.
Pain points: Traditional component polarity detection mainly relies on manual inspection and rule-based machine vision systems. Manual inspection is inefficient and prone to missed detections and misjudgments, with high labor costs. Rule-based machine vision systems require engineers to write complex detection rules based on the characteristics of components. When the product model changes or the component specifications change, the rules need to be rewritten, and the changeover time is long, usually taking several hours or even days. Moreover, due to the differences in the appearance and production processes of components, it is difficult for the rules to cover all situations, resulting in a false alarm rate of over 50%, which seriously affects the production efficiency and product quality. In addition, in some industries with strict quality requirements, such as automotive electronics and medical electronics, the compliance detection requirements are high, and traditional methods are difficult to meet the requirements.
Technical principle
WeLinkirt uses advanced deep learning algorithms and visual foundation models to solve the problem of false alarms for component polarity reversal. Deep learning algorithms can automatically learn the characteristics and patterns of components, rather than relying on manually written rules. The visual foundation model has a powerful feature recognition ability and can accurately identify the characteristics of components, such as appearance, size, and color. Through a large number of positive sample learning, the model can quickly adapt to different types of components and production processes.
- Data collection: Use high-resolution cameras to collect images of PCBA boards to ensure that the images clearly and accurately reflect the characteristics of components.
- Feature extraction: Deep learning algorithms extract the features of components, such as shape, texture, and color, from the collected images.
- Model training: Using APDT positive sample/few sample learning technology, only 1 - 20 good product images are needed to train the model, enabling the model to accurately identify the polarity of components.
- Semantic false alarm filtering: Through semantic understanding and analysis, the detection results are filtered to remove false alarm information and improve the accuracy of detection.
WeLinkirt's solution and products
WeLinkirt provides the DaoAI AI AOI software system and the DaoAI World model to solve the problem of false alarms for component polarity reversal. The DaoAI AI AOI software system has a powerful visual foundation model and can achieve 0-code automatic programming in 5 minutes for a good product, greatly shortening the changeover time. The APDT positive sample/few sample learning technology enables the model to quickly adapt to new components and production processes, reducing the training time and cost. The semantic false alarm filtering function can effectively remove false alarm information and improve the accuracy of detection. The DaoAI World model, as a unified base, has the capabilities of semantic understanding, cross-scenario generalization, and continuous learning from production line feedback. It can be deployed locally and supports multiple methods such as SDK / API / Docker to ensure that the data does not leave the factory, meeting the enterprise's requirements for data security and privacy.
WeLinkirt's AI vision technology makes component polarity detection more accurate and efficient.
Quantitative results: After adopting WeLinkirt's solution, the false alarm rate of component polarity reversal detection has been reduced by more than 80%, the missed detection rate is controlled within 1%, and the detection rate reaches over 99%. The changeover time has been shortened from several hours or even days to less than 5 minutes, greatly improving the production efficiency. At the same time, due to the improvement of detection accuracy, the quality and stability of products have been significantly improved, meeting the compliance requirements of the industry.