|
| MOQ: | 1pcs |
| Standard Packaging: | Paper pack |
| Delivery Period: | within 1~5days |
| Payment Method: | T/T, Western Union, MoneyGram |
| Supply Capacity: | 100000pcs |
Post wave soldering AOI for bottom SMT Equipment ALPHA Z5100
Core advantages
AI algorithm,minimalist programming
1.Combining the successful experience of image defect detection and classification in the extensive semiconductor industry and deep learning network,it can be compatible with the morphological changes of solder points,by training massive PCBA defect data,it can automatically identify devices/solder positions and defect types and solve the problems of complex traditional AOI operation long debugging time higt miscalculation and poor consistennel operation.
Refined defect classification
1.The ledaing in-depth learning target detection model can solve the problem of only OK/NG rough classification in traditional algorithms by accurately locating defect locations and accurately classifying defects,Achieve the effect of very little manual re-judgment and help the factory to save manpower.
AI Model lndependent learning evolution
1.Zhonghui's self-developed in-depth learning training platform can regularly collect production line production data so that AI model can continuously learn incremen-tally so that the equipment can always maintain high detection and low misjudgment,and ensure the continous normal production of the factry
Efficient data asset system
1.Excellent mass data technology ability can furthuse use the AOI machine data to play a role.On this basis,it can expand the defect analysis,process,quality analysis and other follow -up applications,to create a closed-loop data system ,help the factory sustainable to improve the quality of handicrafts.
|
|
| MOQ: | 1pcs |
| Standard Packaging: | Paper pack |
| Delivery Period: | within 1~5days |
| Payment Method: | T/T, Western Union, MoneyGram |
| Supply Capacity: | 100000pcs |
Post wave soldering AOI for bottom SMT Equipment ALPHA Z5100
Core advantages
AI algorithm,minimalist programming
1.Combining the successful experience of image defect detection and classification in the extensive semiconductor industry and deep learning network,it can be compatible with the morphological changes of solder points,by training massive PCBA defect data,it can automatically identify devices/solder positions and defect types and solve the problems of complex traditional AOI operation long debugging time higt miscalculation and poor consistennel operation.
Refined defect classification
1.The ledaing in-depth learning target detection model can solve the problem of only OK/NG rough classification in traditional algorithms by accurately locating defect locations and accurately classifying defects,Achieve the effect of very little manual re-judgment and help the factory to save manpower.
AI Model lndependent learning evolution
1.Zhonghui's self-developed in-depth learning training platform can regularly collect production line production data so that AI model can continuously learn incremen-tally so that the equipment can always maintain high detection and low misjudgment,and ensure the continous normal production of the factry
Efficient data asset system
1.Excellent mass data technology ability can furthuse use the AOI machine data to play a role.On this basis,it can expand the defect analysis,process,quality analysis and other follow -up applications,to create a closed-loop data system ,help the factory sustainable to improve the quality of handicrafts.
