Mineral sorting machine with AI technology can distinguish small differences of minerals ,like texture,color,shape,luster,etc.This expands the sorting range and material types, solves the problems of the current color sorter machine.
Packing :
Wooden casesBrand :
MINGDEMOQ :
1 setOrigin :
ChinaColor :
Customized
Mingde Optoelectronics artificial intelligence ore sorting equipment take the lead in introducing artificial intelligence methods such as deep convolutional neural networks (CNN) in the field of visible light photoelectric sorting to analyze and process material images,and through CNN partial connection, weight sharing, multiple convolution kernels and other methods ,during the training process, the multi-dimensional features of materials are automatically extracted to establish a database, whose sorting effect is far better than traditional photoelectric methods.
AI Mineral Sorter ore sorting machine Technical Advantages:
1.Introduced artificial intelligence methods CNN in the field of visible light photoelectric sorting to analyze and process material images.
2.Al photoelectric sorting technology can automatically extract the multi-dimensional characteristics of materials, like texture, shape, color, quality, Luster, etc., which greatly improves the sorting effect, expands the sorting scene and material types, to meets the market diversification and personalized sorting requirements, and solves the problem of limited color sorting materials in the current color sorter market.
3.Photoelectric sorting requires high real-time performance, while CNN operation is relatively slow. In this regard, we adopt the model compression technology to accelerate CNN operation speed and greatly improve the recognition efficiency.
4.In view of the situation that many mineral materials cannot obtain massive data, our company adopts transfer learning technology and industrial image sample enhancement technology to ensure the recognition accuracy of non-massive data training.
5.The Al sorting machine uses a gigabit camera to transmit image data to a multi- GPU computing platform, which adopts CNN to analyze material types and accurately identify material surface features and texture structures.
Technical Data:
Model NO. | Mineral particle sce (unit:cm) |
Capacity (Unit:T/H) |
Air pressure (Unit:Mpa) |
Sorting aecuracy (Unit:%) |
Ootimal take-out ratio |
Power (Unit:Kwl |
Dimension (Unit-mm) |
Weigh (Unit:KG) |
MAI-D4 | 3<d ≤ 6 | 30-45 | 0.55 | 96 | 10:1 | 4.5 | 4000*2650*1760 | 2100 |
1<d ≤ 3 | 10-12 | 0.55 | ||||||
0.5 <d ≤1 | 6-8 | 0.5 | ||||||
MAI-D6 | 3≤ 6 | 45 -68 | 0.55 | 96 | 10:1 | 5.5 | 4000*3670*1760 | 2350 |
1<d ≤3 | 15-18 | 0.55 | ||||||
0.5 <d≤1 | 9-12 | 0.5 |
The following ores we can sort:
Our Advantages:
1.Mingder AI mineral sorting machines can establish separation mode according to customers' requirements.
2.Independent research and development software ,Close down type mechanism, defending powder into inside of machine.
3.Flexible track-type material conveying system, with small drop, large output, and suitable for the sorting of more materials.
4.High intelligence, remote debugging, smart monitoring, remote service and software upgrading.
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