Mingde Optoelectronics Artificial Intelligence Sorting equipemnt take the lead in introducing Artificial intelligence methods such as deep convolutional neural networks(CNN) In the field of visible light photoelectic 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.

Model	Mineral particle size (cm)	Capacity (T/H)	Air pressure (Mpa)	Sorting accuracy	Optimized Carryover ratio	Power(kW)	Dimension(mm)	Weight(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-D5	3<d≤6	38~52	0.55	96	10:1	5.0	4000*3160*1760	2250
	1<d≤3	12.5~15	0.55
	0.5≤d≤1	8~10	0.5
MAI-D6	3<d≤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
MAI-S4	3<d≤6	35~50	0.55	99.8	20:1	6.0	4850*2650*2750	3000
	1<d≤3	12~14	0.55
	0.5≤d≤1	7~9	0.5
MAI-S5	3<d≤6	44~60	0.55	99.8	20:1	6.5	4850*3160*2750	3250
	1<d≤3	13.5~16	0.55
	0.5≤d≤1	9~11	0.5
MAI-S6	3<d≤6	48~75	0.55	99.8	20:1	7.0	4850*3670*2750	3500
	1<d≤3	17.5~21	0.55
	0.5≤d≤1	10.5~14	0.5

the above index is based on 15% impurity content quartz as an example,the specific index varies with the Material and impurity content.

Technical Advantages

1,Introduced artifical Intelligence methods such as deep convolutional neural networks(CNN) in the Field of visible light photoelectric sorting to analyze and process material Images.

2,AI 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 current color sorter market.

3,Photoelectric sorting Requires high real-time performance,while CNN operation is relatively 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 lerning technology and industrial image sample enhancement Technology to ensure the recognition accuracy of non-massive data training.

5, the AI sorting Machine uses a gigabit camera to transmit image data to multi-GPU computing Platform,which adopts CNN to analyze material types and accurately identify Material surface features and texture structures.

Product feature of AI sorting machine

1,it's the first to Introduce artificial intelligence means of neural network in the field of Sorting,which solves the problem that color sorting machine can only separate According to simple criteria.

2, the sorting mode is Established according to users' sorting requirements to meet users' diversified and personalized sorting requirements.

3,We own the Self-developed software system and closed machine structure, the main internal Components are all imported,which can adapt to the harsh environment Requirements like high dust,high pollution, and high corrosion in the industry and mining area.It has a wider application range and longer life.

4,Flexible track-type Material conveying system,with samll drop,large output,and suitable for the Sorting of more materials.

5,the vibrating feeding Part and the main part of the equipment adopt a split structure to avoid the Impact of vibration generated during the feeding process on the host and make the equipment operation more stable.

6, the sorting effect Can be continuously improved by learning mode, and the deep learning mode can Be developed.

7,High Intelligence,remote debugging,smart monitoring,remote service and software Upgrading.

Sortable ores

Applicable sorting ore Materials

Talc, wollastonite,calcite, Quartzite

Fluorite, Potassium Feldspar, Magnesite, Lithium pyroxene

Phosphate ore, Gold ore, High crystal silicon, oxide copper ore

Barite, bauxite,lead-zinc Ore and fluorite, barite lead-zinc ore

Not limited to the above Ores, eye visible distinctions are applicable to AI sorting machines!