Photoelectric Retro-Reflective Sensor WL2SG-2F3235 IP67 Rated Fast Response Time < 0.5 ms SICK
The Photoelectric Retro-Reflective Sensor WL2SG-2F3235 is designed for precise object detection in industrial automation
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- Supply voltage: 10 V DC to 30 V DC
- Current consumption: 20 mA
- Switching output: PNP, dark switching mode
- Maximum output current: ≤ 50 mA
- Response time: < 0.5 ms
- Switching frequency: 1000 Hz
- Connection: Cable with M8 male connector, 4-pin, 200 mm length
- Cable material: Plastic, PVC
- Cable diameter: 3 mm
- Housing material: Plastic ABS/PC
- Optics material: Plastic PMMA
- Enclosure rating: IP67
- Operating temperature range: -20 °C to +50 °C
- Storage temperature range: -40 °C to +75 °C
- Wave length: 640 nm
- Light spot size: Ø 12 mm at 250 mm distance
- MTTFD: 1,987 years
- Dimensions (H x L x W): 22mm x 14mm x 8mm
- Weight: 0.1 kg
- Photoelectric retro-reflective sensor with autocollimation/coaxial optics
- Visible red light with PinPoint LED light source
- Sensing range up to 1.2 meters without reflector
- AutoAdapt feature for optimized sensing
- Dark switching PNP output with fast response time under 0.5 ms
- IP67 rated housing for dust and water protection
- Polarisation filter included for enhanced detection
- Cable connection with M8 male connector, 4-pin
- Installation should be performed by qualified personnel familiar with electrical and sensor systems to ensure proper setup and safety compliance.
- Industrial automation for object detection
- Detecting transparent objects in manufacturing
- Safety and control systems requiring precise sensing
- AS/NZS 3000 – Wiring Rules (Electrical Installation)
- Low power consumption with 20 mA current draw
- Durable IP67 housing reduces waste by extending product life
- Complies with China-RoHS for hazardous substance restrictions
Standard manufacturer warranty applies; please refer to SICK for specific terms.
The SICK WL2SG-2F3235 Photoelectric Retro-Reflective Sensor is available for immediate purchase, ideal for industrial automation and object detection applications.
