Detecting aluminum electrolytic capacitors with a diameter of Φ 10-18
The AI capacitor appearance inspection machine uses processes such as image acquisition, image processing, image annotation, AI algorithm models, and software scheduling to detect appearance defects in aluminum electrolytic capacitors with a diameter of Φ 10-18, rejects defective products, and calculate and divide good products into boxes.
Maximum speed: 60-100 pieces/minute
Detection accuracy: 0.1mm
Detection area: 360 °
- Defect Detection
- Product Advantages
With the Al algorithm based on deep learning, focusing on network cascading, equipped with KVIS-cloud platform, it provides customers with targeted convolutional neural network technology solutions to more accurately reflect image features.
The use of AI-specific NPU chips guarantees powerful computing power for product detection under high-speed operation and accelerated reasoning.
Provide full-process 0-code model training, optimization and model prediction services, template loading, one-click model change.
Fusion of image multimodal information input to achieve product defect detection and identification.
Multi-node layout to process data efficiently in parallel computing mode, avoiding single node load data operations.
superior arithmetic power to improve the whole machine image acquisition and transmission capabilities, and a high degree of synergy with software scheduling.
