Key Findings
Changan Automobile has commissioned a vision AI gluing robot for painting operations at its Liangjiang Plant, resulting in a production line efficiency increase of roughly 8% in terms of jobs per hour (JPH). The integration of AI visual recognition allows for real-time monitoring of the gluing state with millisecond-level response times, shifting quality management from post-production inspection to active process monitoring.
Technical Details
The system addresses the complexities of the PVC undercoat gluing process, which is typically characterized by tight working spaces, complex application paths, and wide variations between vehicle models. To overcome traditional robotic limitations regarding nozzle calibration, angle adjustment, and trajectory fine-tuning, Changan implemented an AI visual inspection system powered by deep Convolutional Neural Networks (CNN).
Following hundreds of alignment tests, sealant validations, and trajectory debugs, the system optimizes the following parameters:
- Robot coordinates
- Nozzle posture
- Travel paths
These optimizations enable the robot to adapt to the specific undercoat gluing requirements of various vehicle models. Furthermore, the system’s risk detection and reporting accuracy now exceeds 99.5%.
Background & Context
The deployment of the painting gluing robot is part of a broader strategy to integrate AI vision technology across multiple production stages. Changan had previously deployed visual inspection systems for stamping and welding operations at the Liangjiang Plant.
Significance & Outlook
The successful implementation of this technology alleviates existing bottlenecks in the PVC process. By extending AI vision into the painting stage, Changan has expanded the scope of AI-driven manufacturing execution and quality assurance within its production facilities.
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