Machine Vision
FPC defect detector
PCB and flexible circuit board appearance inspection applications
Machine Vision
PCB and flexible circuit board appearance inspection applications
The automatic appearance inspection equipment, with a number of technologies such as optical inspection, Cam resolution and AI deep learning, has high precision, high resolution, high efficiency and high board absorption success rate, automatic loading and unloading with fully automatic inspection, the efficiency can reach five times that of manual inspection equipment.
It can be widely used in PCB, FPC and other areas of the appearance of flexible circuit board inspection.
Self-compiled autonomous learning algorithm, fine-tuned to be compatible with all types of circuit appearance detection.
Model | FPC-P500 | ||
FPCB Size range | <500*600mm | FPCB Thickness range | 0.2-1mm |
Power supply | AC220V 50/60HZ Less than 7KVA | Equipment size | 1962*1490*1528 |
Ambient temperature | 22 plus or minus 2 degrees / 55 plus or minus 5% (no crystallization) | Equipment safety regulations | Conforms to CE safety standards |
Applicable PCB thickness | 0.5mm~3.0mm | ||
Testing Method | AI intelligent deep learning. Through speech segmentation. Classification network and other regular defects autonomous analysis, learning, can significantly improve the efficiency and accuracy of recognition detection | ||
Test circuit board | FPCB SMT Front | Webcam | High-speed color line-scan camera + high-resolution line-scan lens |
Accuracy of defect detection | Missing detection rate Less than 0.3% | Light source | Line coaxial direct + diffuse light direct DOME type |
Programming Mode | Gerber imports semi-automatic recipe, manual intervention is greatly reduced | Special Features | Support server database, review system functions |
Bar code system | Automatic barcode recognition | Server Mode | Central data server is used to centralize and manage several AVI data |
Operating System | Windows10 64-bit Professional Edition | Check Result Output | 22" LCD monitor |
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Email:jszy@jscostarnet.com