AI AOI Software System Detects Component Polarity Reversal in PCBA

At the 2026 World Artificial Intelligence Conference, industrial AI controllers and visual inspection became the focus. WeLinkirt's AI AOI software system plays an important role in detecting component polarity reversal in the electronics/PCBA industry with its advanced technology.

The electronics/PCBA industry is developing rapidly, and the complexity and precision of products are constantly increasing. A certain electronics manufacturing enterprise's PCBA production line mainly produces circuit boards for various consumer electronic products. A key process in its production line is component placement. In this process, various electronic components need to be accurately placed on the circuit board. Among them, the correct installation of component polarity is particularly important. If the component polarity is reversed, it will cause abnormal functions of the circuit board and even damage the entire product. Therefore, accurate detection of component polarity is a key link to ensure product quality.

Pain Points: Why Is This Hurdle So Hard to Cross?

When using traditional detection methods, the enterprise faced many difficulties. First, the miss rate was relatively high, reaching about 2%. This means that for every 100 circuit boards produced, there may be 2 with component polarity reversal problems flowing into the market, bringing after-sales risks and brand losses to the enterprise. Second, the false alarm rate was also not to be underestimated, as high as 15%. A large number of false alarms required manual re-judgment, increasing the man-hours for manual re-judgment. On average, an additional 8 hours of manual work were required per day for re-judgment, which not only increased labor costs but also reduced production efficiency. Moreover, the change-over downtime was long. Every time the product model was changed, it took 2 hours for debugging, seriously affecting the production rhythm of the production line. In addition, with the continuous improvement of industry standards, the enterprise faced higher compliance risks. If it could not detect component polarity reversal problems accurately and in a timely manner, it might face customer complaints and regulatory penalties.

From the process level, the polarity marks of components are usually very small, and the imaging effects vary greatly under different lighting conditions, which makes it difficult for traditional rule-based AOI to accurately identify. From the imaging perspective, due to the dense components on the circuit board, there may be occlusion and reflection phenomena, which affect the imaging quality and lead to false and missed detections. In terms of materials, some components have similar appearances but only different polarity marks, which increases the difficulty of detection. In terms of production rhythm, the enterprise pursues high-efficiency production, and traditional detection methods are difficult to meet the needs of rapid detection, resulting in long change-over downtime. Combining with the hot topics of the 2026 World Artificial Intelligence Conference, traditional detection methods can no longer meet the needs of industrial intelligent development, and advanced industrial AI technology needs to be introduced to solve these problems.

Technical Principle

WeLinkirt's AI AOI software system is based on the visual basic model for feature recognition. It uses advanced deep-learning algorithms to establish a feature model of component polarity by learning from a large number of good product samples. During the detection process, the system compares the real-time captured image with the feature model to accurately determine whether the component polarity is correct. The function of automatic programming for one good product in 5 minutes with zero-code of this system greatly shortens the modeling time. By providing 1-20 good product samples and using the APDT positive-sample/few-sample learning technology, the system can quickly learn the features of components and complete the modeling without complex programming. In addition, the semantic false-alarm filtering function can intelligently analyze the detection results and filter out false-alarm information caused by factors such as lighting and occlusion, improving the detection accuracy.

Compared with traditional rule-based AOI, traditional rule-based AOI detects based on preset rules, which has poor adaptability to complex and changeable component polarity detection scenarios and is prone to missed and false detections. The AI AOI software system has stronger adaptability and learning ability and can accurately detect according to different imaging conditions and component features. Compared with manual visual inspection, manual visual inspection is inefficient, prone to fatigue, and greatly affected by subjective factors. The AI AOI software system can achieve uninterrupted 24-hour detection with high detection accuracy, greatly improving detection efficiency and accuracy.

Typical Application Scenarios

• Component polarity reversal detection: The system identifies the appearance features and polarity marks of components to determine whether the component polarity is correct. The difficulty lies in the small polarity marks of components, which are easily affected by imaging, and the different ways of marking polarity for different types of components.
• Component missing detection: The visual basic model is used to identify the position and features of components on the circuit board to determine whether there are any missing components. The difficulty lies in the dense components on the circuit board, which may cause occlusion and affect the detection accuracy.
• Component mis-placement detection: Compare the actual placement position and model of components with the design requirements. The difficulty lies in the similarity of some components' appearances, with only slight differences in features, requiring high-precision recognition algorithms.
• Solder joint quality detection: Analyze the shape, size, and color of solder joints to determine whether the solder joint quality is qualified. The difficulty lies in the complex shape of solder joints, and the imaging is greatly affected by lighting and angles.

Implementation Case

A medium-sized electronics manufacturing enterprise produces about 500,000 circuit boards annually. Before introducing WeLinkirt's AI AOI software system, the enterprise used a combination of traditional rule-based AOI and manual visual inspection for detection, with a miss rate of 2%, a false alarm rate of 15%, and a change-over downtime of 2 hours. During the implementation process, WeLinkirt's technical team completed the installation, debugging, and training of the system in only 3 days. After going live, the detection effect was significantly improved. The miss rate was reduced to <0.2%, the false alarm rate was reduced by −80%, and the change-over downtime was shortened to 5 minutes.

The AI AOI software system brings an efficient and accurate solution to the detection in the electronics/PCBA industry.

WeLinkirt's Solution and Product

WeLinkirt's AI AOI software system provides a complete solution for the electronics/PCBA industry with its core capabilities. In terms of modeling, it uses the technology of automatic programming for one good product in 5 minutes with zero-code and APDT positive-sample/few-sample learning to quickly and accurately establish component feature models. In terms of change-over, the system can quickly adapt to the detection needs of different product models, and the change-over time is shortened to 5 minutes. In terms of deployment, it supports 100% local private deployment of SDK/API/Docker, and the data does not leave the factory, ensuring the security of enterprise data. At the same time, the software system can be integrated with the enterprise's existing production management system to achieve real-time data sharing and automated management of the production process. In addition, other product lines of WeLinkirt, such as DaoAI 2D/3D AI AOI equipment and DaoAI robot vision, can also be used in conjunction with the AI AOI software system to further improve the enterprise's detection and production capabilities.

By introducing the AI AOI software system, the enterprise has achieved remarkable results in many aspects. In terms of detection accuracy, the miss rate has been reduced from 2% to <0.2%, greatly improving product quality and reducing after-sales risks. In terms of detection efficiency, the false alarm rate has been reduced by −80%, reducing the man-hours for manual re-judgment and improving production efficiency. The change-over downtime has been shortened from 2 hours to 5 minutes, improving the utilization rate of the production line and reducing production costs. At the same time, the local private deployment of the system ensures the security of enterprise data and meets industry compliance requirements.