Photoelectric Retro-Reflective Sensor WL100L-F1131 Dual Lens System IP65 Rated SICK
The WL100L-F1131 Photoelectric Retro-Reflective Sensor is designed for high-speed object detection in industrial automation.
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- Functional principle: Photoelectric retro-reflective sensor with dual lens system
- Sensing range: 0.08 m to 10 m (max 12 m)
- Light source: Laser, 650 nm wavelength, Laser class 1
- Supply voltage: 10 V DC to 30 V DC with ±10% ripple
- Current consumption: 30 mA
- Switching output: PNP, max output current ≤ 100 mA
- Switching mode: Light/dark selectable via rotary switch
- Response time: < 0.25 ms
- Switching frequency: 2,000 Hz
- Connection: 3-wire cable, 2 m length, PVC insulation
- Housing material: Plastic (ABS/PC/POM), Optics material: PMMA
- Enclosure rating: IP65
- Operating temperature: -10 °C to +50 °C
- Storage temperature: -40 °C to +70 °C
- Includes stainless steel mounting bracket and reflector
- Dimensions (H x L x W): 31mm x 20mm x 11mm
- Weight: 0.1 kg
- Photoelectric retro-reflective sensor with dual lens system for minimum distance to reflector
- Visible red laser light source with 650 nm wavelength
- Adjustable potentiometer with 270° rotation
- Selectable light/dark switching mode
- High switching frequency of 2,000 Hz
- IP65 rated enclosure for dust and water resistance
- Includes stainless steel mounting bracket and reflector
- Installation by qualified personnel is recommended to ensure proper alignment and electrical connection according to safety standards.
- Industrial automation for object detection
- High-speed object detection
- Small object detection in manufacturing lines
- AS/NZS 4801 – Occupational Health & Safety Management Systems
- Energy-efficient laser technology reduces power consumption
- Durable IP65 housing extends product lifespan, reducing waste
Standard manufacturer warranty applies. Please refer to SICK for detailed warranty terms.
The SICK WL100L-F1131 Photoelectric Retro-Reflective Sensor is available for immediate purchase, ideal for industrial automation and object detection applications.
