At present, most manufacturers use manual capping inspection, which is time-consuming and labor-intensive, and can only conduct random inspections when the batch size is large.

The focusing method is manual focusing.

AI intelligent big data algorithm

Adaptive learning: The algorithm has self-learning ability and can continuously optimize the model based on a large number of on-site samples, adapt to different product specifications and production environments, and improve the flexibility and reliability of detection.

Real-time processing: The AI algorithm can quickly process thermal imaging data and judge whether the product is qualified in real time to ensure efficient operation of the production line and reduce downtime.
Data-driven decision-making: Through big data analysis, the algorithm can generate production data reports to help companies optimize production processes and improve overall efficiency and quality control.
Fast specification switching: The algorithm supports fast switching of different product specifications, completes configuration adjustments within 30 seconds, reduces downtime and debugging time, and improves production efficiency.
Multi-defect identification: The algorithm can simultaneously detect multiple sealing defects, such as no cover, no aluminum foil cover, crooked cover, aluminum foil warping, bottle mouth clamping, abnormal sealing power, loose cover, etc., with comprehensive coverage and low missed detection rate.

Core advantages

Reliable operation: Detection pass rate ≥ 99.999%, comprehensive detection false kick rate ≤ 0.3%.
Quick specification switching: 30 seconds, all production data can be saved and configured. When switching to the original specification, only one button is required to confirm, reducing the startup and debugging time.

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