Automobile Painting Surface Defect Detection: High - precision leap from manual to AI

In the automotive manufacturing industry, the painting process is crucial for the appearance and quality of products. Traditional detection methods struggle to meet high-precision requirements. WeLinkirt provides an effective solution for automobile manufacturers with advanced AI vision technology.

User scenario: In the painting production line of a leading automobile manufacturer, the main products are various automobile bodies. The detection objects are defects on the automobile painting surface, such as scratches, bubbles, and sags. These defects not only affect the appearance of the automobile but also may reduce the protective performance of the body. Therefore, accurate detection of defects is crucial.

Pain points: Under the traditional detection method, manual re-judgment is inefficient and has a relatively high rate of missed detections and false alarms. The missed-detection rate of manual detection is about 5%, and the false-alarm rate reaches 30%. This means that a large number of defects may flow into the next process, and it also increases the unnecessary re-inspection workload. Moreover, manual detection requires a large number of samples. When the samples are insufficient, it is difficult to guarantee the detection accuracy. In addition, when changing the production line model, it takes about 30 minutes to manually adjust the detection standards and processes, which seriously affects production efficiency.

Technical principle

WeLinkirt uses advanced AI algorithms and imaging technologies to solve the above problems. In terms of algorithms, deep-learning algorithms are used to extract and analyze the features of the painting-surface images. The deep-learning network can automatically learn the feature patterns of defects and continuously optimize the model through a large amount of training data, so as to accurately classify and identify different types of defects. For example, the Convolutional Neural Network (CNN) can effectively capture local features in the image and has high sensitivity to small defects such as scratches and bubbles.

• In terms of imaging technology, the self-developed 3D camera of the DaoAI 2D / 3D AI AOI equipment plays an important role. The 3D camera can obtain the three-dimensional topography information of the painting surface. Through the three-dimensional topography reconstruction technology, the real situation of the painting surface is presented in the form of a three-dimensional model. This enables the system to not only detect visible surface defects but also discover defects hidden inside the coating, such as uneven coating thickness.
• At the same time, the three-dimensional topography information can provide richer feature data, which helps to improve the accuracy of defect classification. For example, for sag defects, by analyzing the height changes in the three-dimensional topography, the severity and scope of the sag can be more accurately determined.
• In addition, WeLinkirt's algorithm also combines semantic-understanding technology, which can conduct a more in - depth analysis of defects. Semantic understanding allows the system to understand the meaning and features of defects, so as to better distinguish real defects from false alarms and further improve the detection accuracy.

WeLinkirt's solution and product introduction

WeLinkirt provides a combined solution of the DaoAI AI AOI software system and the DaoAI 2D / 3D AI AOI equipment. The DaoAI AI AOI software system has powerful feature-recognition capabilities. Based on the visual basic model, it can realize 0-code automatic programming within 5 minutes for one good product. Its APDT positive-sample / few-sample learning function only requires 1-20 good products for model training, greatly reducing the requirement for the number of samples and maintaining high accuracy even when the samples are insufficient. At the same time, the semantic false-alarm filtering function can effectively reduce false alarms and improve detection efficiency.

The combination of the DaoAI AI AOI software system and the DaoAI 2D / 3D AI AOI equipment provides an efficient and accurate solution for automobile painting surface defect detection.

The self-developed 3D camera and three-dimensional topography reconstruction technology of the DaoAI 2D / 3D AI AOI equipment provide more comprehensive and accurate information for the detection work. This equipment can detect hidden solder joints, coplanarity, and micron-level topography, meeting the high-precision requirements of automobile painting surface defect detection. In terms of production-line deployment, the system supports SDK / API / Docker methods and can achieve 100% local private deployment, ensuring that data does not leave the factory and safeguarding data security.

Quantitative results: After introducing WeLinkirt's solution, the detection effect has been significantly improved. The defect detection rate has increased to 99.2%, and the missed-detection rate has been reduced to less than 0.8%, effectively preventing defective products from flowing into the next process. The false-alarm rate has been reduced by -68%, greatly reducing the unnecessary re-inspection workload. The production-line model-changing time has been shortened from 30 minutes to 5 minutes, improving production efficiency.

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.