Ai Visual Inspection System For Biodegradable Bagasse Round Plate
| System operating parameters |
| Dimensions | See the design drawings for details | Power and frequency | 220V 20A 50HZ |
| Total power | 3kw ~ 4kw | Air pressure | 0.5~0.8MPa Purify and oil-free |
| Working temperature | -20℃ ~ 60℃ | Working humidity | Below 50% relative humidity |
Inspection standards
| Item | Camera | Position | Inspect | Precision | Accuracy | Speed |
| KVIS | 1 | bottle mouth | Black sopt,dirt,size,burr,flash,hole,label position | >0.5mm | 99% | 60pcs/min |
| 4 | upper part of the bottle | >0.5mm |
| 4 | Lower part of the bottle | >0.5mm |
| 1 | Bottom area | >0.5mm |
Application of camera
- Front area detection: The reverse side of the incoming material on
the tray is upward, and it is suspended in the air by the negative
pressure belt. A set of industrial cameras is arranged directly
under the tray, and the use of visual light source is used to
collect images of the front area for processing.
- Reverse area detection: After the front area is detected, the
pallet falls on the rear belt smoothly, and the reverse side is
still facing up. A set of industrial cameras is arranged directly
above the pallet, and with the use of visual light source, the
image of the reverse area is collected for processing.
- Bevel area detection: The reverse side of the pallet is facing
upwards, and 4 sets of industrial cameras are arranged in a circle
above the pallet, which are used in conjunction with the visual
light source to collect images of the bevel area for processing
Configuration parameters
| Specifications | Q'ty |
| Industrial camera(KEYE) | 10 | SET |
| Plane lens(AZURE/HK) | 10 | PCS |
| Frequency flash source(OEM) | 3 | SET |
| Photoelectric sensor(SICK) | 1 | PAIR |
| High-speed solenoid valve(SMC) | 1 | PCS |
| Industrial power(MEAN WELL) | 3 | PCS |
| Circuit trigger board(KEY-PC-2.1) | 1 | PCS |
| Industrial computer(VECOW) | 1 | SET |
| Hd touch device(AOC 21’) | 1 | SET |
Advantages of ai algorithm
- Accuracy of results: Compared with traditional vision algorithms,
the stability of deep learning algorithms is greatly improved, and
it can adapt to general disturbances in the environment and
background. At the same time, the accuracy of the algorithm is also
higher than that of traditional vision algorithms;
- Algorithm versatility: For different defects, only a small number
of defect samples need to be collected. After sufficient training,
different defect samples can be automatically identified. The
algorithm adopted is a unified algorithm framework;
- Development timeliness: Since there is no need to develop
algorithms for different defects, the entire development cycle is
greatly shortened, and general visual defect projects only need 2-3
hours to go online for testing;
- Environmental reliability: Due to the guarantee of GPU computing
power, the entire system can work in a high-temperature environment
for a long time with sufficient computing power margin.
Advantages of big data cloud platform
- The model has wide applicability: by distinguishing whether there
is a template or not, it can basically cover the detection
requirements of surface defects, and new defect models can be added
arbitrarily;
- The robustness of the model is good: through the system signal
consistency adjustment, it is basically guaranteed that there is no
need for fine-tuning on site, and it can run normally when it goes
online;
- Fewer training samples are required: In the case where a large
number of defect samples cannot be obtained, the automatic
synthesis of samples can be used to meet the needs of deep learning
with large data volume training samples;
- Fast model reasoning speed: Control the cost of model deployment,
and provide customers with a cost-effective deployment platform as
much as possible on the premise of meeting the needs.
