Intelligent Parts Counter
Equipment Performance Specifications
Specification
| Model | EWB-PMA/B |
| External Dimensions | L1100 × W1385 × H2010 mm (Actual dimensions subject to physical product) |
| Counting Speed | 6–10 seconds |
| Reels per Cycle | 7″ / 4 reels per cycle 10–15″ / 1 reel per cycle |
| Accuracy | 99.99‰ |
| Tube Max. Voltage | 80 kV |
| Tube Max. Current | 1000 μA |
| Component Size | Min. CHIP size: 01005 |
| Feed/Inspection Height | 8–80 mm |
| Inspection Height | 8–60 mm |
| Barcode Scanning | Handheld scanner (Optional: Auto-scan) |
| Power Supply | 220 VAC ±10% 50/60 Hz |
| Database | ERP System Integration, MES System |
| Power Consumption | 1.0 kW |
| Discharge Method | Forward In / Forward Out |
| Operating Temperature | < 40°C |
| Relative Humidity | Standard 70% @ 32°C |
| Safety | Compliant with national safety standards: < 0.4 μSv/h |
Software AI Algorithm
A neural network is typically composed of numerous interconnected neurons. The overall behavior of a system depends not only on the characteristics of individual neurons but may also be primarily determined by the interactions and connections between these units.
Artificial neural networks possess adaptive, self organizing, and self-learning capabilities.
Nonlinear relationships are a universal characteristic of nature. The wisdom of the brain is a nonlinear phenomenon. Artificial neurons exist in two different states: activated or inhibited. This behavior is mathematically expressed as a nonlinear artificial neural network relationship.
Under certain conditions, the evolution direction of a system will depend on a specific state function.
Key Benefits
Why Use X-RAY Parts Counter?
Working Principle
Intelligent Warehouse Operation Process
Hardware Function Introduction
Sample Counting Images 
