Photoelectric Retro-Reflective Sensor WLG12-V537 8 Beams IP67 Rated SICK
The WLG12-V537 Photoelectric Retro-Reflective Sensor is designed for precise object detection in industrial automation.
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- Sensor principle: Photoelectric retro-reflective
- Minimum detectable object: 6 mm to 12.5 mm
- Number of beams: 8
- Detection height: 87.5 mm
- Resolution adjustable between 6 mm and 12.5 mm
- Wavelength: 650 nm
- Supply voltage: DC 18 V to 30 V
- Power consumption: 80 mA
- Output current max: ≤ 100 mA
- Output type: PNP, Q dark switching
- Initialization time: 0.6 s
- Connection: Male M12, 5-pin connector
- Housing material: Plastic with PMMA front screen
- Enclosure rating: IP67
- Shock resistance: 10 g, 16 ms
- Vibration resistance: 5 g, 10 Hz to 55 Hz
- Operating temperature: -25 °C to +55 °C
- Storage temperature: -20 °C to +70 °C
- Dimensions (H x L x W): 106mm x 47mm x 62mm
- Weight: 0.23 kg
- Photoelectric retro-reflective sensor with 8 beams
- Minimum detectable object size adjustable between 6 mm and 12.5 mm
- Detection height of 87.5 mm
- Operating range up to 1.5 meters
- High shock and vibration resistance
- IP67 rated enclosure for dust and water protection
- LED indication for operational status
- Reverse polarity and short-circuit protected outputs
- Installation by qualified personnel is recommended to ensure proper electrical connections and sensor alignment for optimal performance.
- Industrial automation for object detection
- Manufacturing process control
- Packaging and material handling systems
- Safety and monitoring in production lines
- Complies with AS/NZS 60950 – Safety of IT and Communication Equipment
- Durable design reduces replacement frequency and waste
- Low power consumption of 80 mA reduces energy usage
- High reliability minimizes downtime and resource waste
Standard manufacturer warranty applies; please refer to SICK's official warranty policy for details.
The SICK WLG12-V537 Photoelectric Retro-Reflective Sensor is available for immediate purchase, ideal for industrial automation and object detection applications.
