



Functional Features
1. AI Deep Learning
Powered by deep learning and multi-dimensional modeling, the system autonomously trains sorting schemes to achieve highly accurate dynamic identification and rejection, effectively handling a wide variety of materials.
2. Multispectral Precision Detection
Integrates multiple light sources, including RGB and near-infrared, enabling easy detection of subtle defects such as color variations, mold, cracks, and contamination.
3. Multi-Dimensional Feature Analysis
Combines data on color, shape, texture, and hardness to construct material profiles accurately. Performs coarse sorting followed by fine sorting to minimize misjudgment rates.
4. Ultra-High-Speed Intelligent Sorting
AI works in tandem with high-speed electromagnetic valves to enable efficient and rapid material sorting, breaking through the speed limitations of traditional systems and boosting production efficiency.
Main Applications
Coffee Bean | Pea Wormhole | ||
Reject |
Accept |
Accept |
Reject |
Pistachio Nut | |||
Reject |
Accept |
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Technical Parameter
Model | HWT-2AI | HWT-3AI | HWT-4AI | HWT-5AI | HWT-6AI | HWT-7AI | HWT-10AI |
Output(t/h) | 0.5-4.0 | 1.0-5.0 | 1.5-6.0 | 2.0-7.0 | 2.5-8.0 | 3.0-9.0 | 4.0-11.0 |
Accuracy(%) | ≥99.99 | ≥99.99 | ≥99.99 | ≥99.99 | ≥99.99 | ≥99.99 | ≥99.99 |
Carryover Rate(Bad:Good) | >20:1 | >20:1 | >20:1 | >20:1 | >20:1 | >20:1 | >20:1 |
Weight(kg) | 680 | 840 | 1000 | 1165 | 1310 | 1505 | 2050 |
Resolution(mm) | 0.015mm | 0.015mm | 0.015mm | 0.015mm | 0.015mm | 0.015mm | 0.015mm |
Air Consumption(m³/min) | 0.5-1.0 | 0.8-1.5 | 1.0-2.0 | 1.3-2.1 | 2.0-3.5 | 2.5-4.3 | 5.0-6.5 |
Power(Kw) | 1.5 | 2.0 | 2.4 | 3.0 | 3.6 | 4.2 | 6.0 |
Dimensian(L*W*H/mm) | 1244*1570*2090 | 1564*1570*2090 | 1864*1570*2090 | 2184*1570*2090 | 2500*1570*2090 | 2814*1570*2090 | 3756*1570*2090 |
Power Input(V/Hz) | 220/50(110/60) | 220/50(110/60) | 220/50(110/60) | 220/50(110/60) | 220/50(110/60) | 220/50(110/60) | 220/50(110/60) |
Keywords: AI Deep Learning Color Sorter