Photoelectric Retro-Reflective Sensor IP66 IP67 IP69K Rated 1000 Hz Switching Frequency SICK
The Photoelectric Retro-Reflective Sensor WL9M4-3N1132 is designed for precise object detection in industrial automation.
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- Supply voltage: 10 V DC to 30 V DC
- Current consumption: 30 mA
- Switching output: NPN, complementary
- Output current max: ≤ 100 mA
- Switching mode: Light/dark switching
- Response time: < 0.5 ms
- Switching frequency: 1,000 Hz
- Connection: 4-wire cable, 2 m length, PVC insulation
- Protection class: III
- Housing material: Plastic VISTAL®
- Optics material: Plastic PMMA
- Enclosure rating: IP66, IP67, IP69K
- Operating temperature: -40 °C to +60 °C
- Storage temperature: -40 °C to +75 °C
- Light spot size at 1.5 m: Ø 45 mm
- MTTFD: 1,530 years
- Mission time: 20 years
- Dimensions (H x L x W): 50mm x 23.6mm x 12.2mm
- Weight: 0.1 kg
- Photoelectric retro-reflective sensor with autocollimation/coaxial optics
- Visible red light with PinPoint LED at 650 nm wavelength
- Single teach-in button for easy adjustment
- High switching frequency of 1,000 Hz and response time under 0.5 ms
- Complementary NPN output with light/dark switching mode
- Robust plastic VISTAL® housing with IP66, IP67, IP69K enclosure rating
- Wide ambient operating temperature range from -40 °C to +60 °C
- Polarizing filter included to reduce false triggering
- Installation by a qualified technician or electrician is recommended to ensure proper wiring and sensor alignment.
- Industrial automation for object detection
- Small object detection in manufacturing processes
- Use in harsh environments requiring IP66, IP67, IP69K protection
- Complies with AS/NZS 3000 – Wiring Rules (Electrical Installation)
- Energy-efficient LED light source reduces power consumption
- Durable design minimizes waste and replacement frequency
Standard manufacturer warranty applies; please refer to SICK for specific warranty terms.
The SICK WL9M4-3N1132 Photoelectric Retro-Reflective Sensor is available for immediate purchase, ideal for industrial automation and object detection applications.
