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Smart Solder Paste Cabinet

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Leenol Smart Solder Paste Cabinet



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Introduction

  • Built in rotating independent refrigeration compartment, air cooling circulation, low power consumption, low noise.
  • Automatic stirring, servo drive, stepless speed regulation.
  • Batch Storage and Retrieval Exit and Entrance, light guidance, photoelectric induction.
  • Industrial grade 360 rotation scanning, hard decoding, high fault-tolerant lD recoqnition.
  • NFC+ fingerprint login system, full-cycle intelligent traceability of human-machine materials and paste permissions.
  • Standard third-party systems are easily connected and support single-machine online operation.

Parameter Name NESTS-TN SP5000 SMART SOLDER PASTE CABINET
Dimensions L*W*H: 1520mm*1450mm*2190mm,Support Customization
Electrical Parameters Voltage:AC220V,Power:1500W(MAX),Optional UPS
Air Source Requirements Air Pressure:0.5MPa,Airflow:2L/min(MAX)
Partition Capacity Number of refrigeration areas: 360Bottles; Number of temperature recovery areas: 72Bottles;
Number of exit and entrance: 12Bottles.500g standard bottled solder paste
Temperature Range Refrigeration temperature range:1℃~10℃ adjustable, uniformity ±3℃
Reheating temperature range: 18 ~28℃(room temperature)
Execution Method Automatic entry and exit, Automatic mixing, Compatible with AGV automatic docking
Recognition System Machine vision recognition
Safety System Equipment safety door, Emergency stop safety system
Error Prevention For Entry and Exit Light guidance,Photoelectric detection error prevention
Temperature and Humidity Monitoring Supports online monitoring of temperature and humidity
Working Noise Less than 55 decibels
System Interface Standard three-party software interface system, Ethernet communication
Intelligent Operation and Maintenance Real-time electronic control, Accurate self-diagnosis of worn parts, Supporting fault alarm,Safe shutdown, and automatic recovery; One-stop intelligent equipment operation and maintenance platform,Supporting online eauipment monitoring and operation and maintenance prediction.

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