| Payment Terms | L/C,D/A,D/P,T/T,Western Union,MoneyGram |
| Delivery Time | 5-8 work days |
| Model | HD2 |
| Belt Width (mm) | 600 |
| Air Nozzle | 256 |
| Air Pressure (MPa) | 0.6~0.8 |
| Core Technologies | <2.0 |
| Voltage | 220V~50/60Hz |
| Brand Name | PHOENIX |
| Model Number | HD AI Optical Sorter |
| Place of Origin | Hefei |
View Detail Information
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Product Specification
| Payment Terms | L/C,D/A,D/P,T/T,Western Union,MoneyGram | Delivery Time | 5-8 work days |
| Model | HD2 | Belt Width (mm) | 600 |
| Air Nozzle | 256 | Air Pressure (MPa) | 0.6~0.8 |
| Core Technologies | <2.0 | Voltage | 220V~50/60Hz |
| Brand Name | PHOENIX | Model Number | HD AI Optical Sorter |
| Place of Origin | Hefei | ||
| High Light | Photoelectric Nuts Sorting Machine ,256 Air Nozzles Nut Sorter ,Photoelectric Nut Sorter | ||
The HD Series Medium-Speed Track-Type Photoelectric Sorting Machine ushers in a new era of nut sorting with its "Golden Speed"! It achieves a dynamic balance between output and anti-crushing performance. Driven by a track speed between high-speed and low-speed models, it realizes the long-term coexistence of efficiency and quality — the crushing rate is reduced by 68% compared with traditional equipment, making high quality control and high output no longer a trade-off.
SHD ultra-clear imaging enables ultra-precise identification of tiny material defects (e.g., small dark spots, small cracks, small mold spots);
The new SHD Ultra-Clear Deep Learning System, with 6 years of sorting model iterations and support from a 10-billion-scale database, can tackle tens of thousands of complex sorting challenges;
Ultraviolet recognition technology can identify materials with distinct fluorescence properties (e.g., moldy materials, toxic substances).
From farm to table, we safeguard the natural flavor with technology. The HD Series — it’s more than just a speed upgrade; it’s a trailblazer in nut sorting!
One-click smart sorting with intelligent simulation technology Real-time dynamic online tracking for a better operating experience
Based on a massive sample database, it can quickly build and iterate deep learning sorting models. It is capable of removing complex foreign objects and defects that traditional algorithms struggle to handle, meeting customers' needs for high-volume, variable, and refined sorting.
The Sample Smart Acquisition Self-Learning System intelligently expands the sample data of rejected materials through methods such as biological sample characteristic marking, morphological marking, associated and non-associated feature marking. It continuously optimizes sorting performance and delivers an ultra-simple operation experience.
Equipped with an industrial-grade FPGA chip, it features faster, more efficient, and smarter operational efficiency. It meets users' needs for changes in different material properties, impurity content, and quality standards, providing a better sorting experience for users.
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| Model | Belt Width (mm) | Air Nozzle | Air Pressure (MPa) | Air Consumption (m³/min) | Voltage | Power (kW) | Dimension (mm) | Unpacked Weight (kg) |
|---|---|---|---|---|---|---|---|---|
| HD2 | 600 | 256 | 0.6~0.8 | <2.0 | 220V~50/60Hz | 6.4 | 3641×1540×2804 | 1376 |
| HD4 | 1200 | 512 | 0.6~0.8 | <4.1 | 220V~50/60Hz | 7.9 | 3641×2140×2804 | 1917 |
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