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Home > Blog > New Release of SICK UD18 Ultrasonic Sensors for Reliable Double Sheet Detection

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New Release of SICK UD18 Ultrasonic Sensors for Reliable Double Sheet Detection

SICK has officially launched its brand-new UD18 series ultrasonic sensors, specially optimized for industrial double-sheet inspection applications. Featuring a streamlined product lineup, this new sensor stands out with super easy commissioning and consistent high-precision detection for various industrial raw materials.

Working on mature ultrasonic sensing principle, the UD18 can precisely identify three statuses: single sheet, double sheet and missing stock, covering paper, cardboard, polished metal and transparent plastic. It runs steadily for detection of thin, glossy and translucent parts, fully satisfying mainstream separation inspection needs on automated production lines.

Core Product Advantages

✅ Flexible Parameter Debugging

The sensor supports quick configuration via official SICK Sentio & Sopas software, together with IO-Link communication, realizing real-time visualized equipment status monitoring and drastically cutting field setup time.

✅ Universal NPN/PNP

CompatibilityOne single sensor model fits both NPN and PNP output modes, eliminating repeated model selection and simplifying spare part inventory management.

✅ Stable Long-term Supply

All specifications of UD18-22DCxxx have no designated replacement versions, guaranteeing continuous stocking for customers’ equipment standardization and later maintenance.


Widely deployed in printing, packaging, papermaking, electronics and automotive industries, UD18 is ideal for feed double-sheet monitoring on printing machines and die-cutting equipment. Thanks to excellent resistance against workshop dust and ambient moisture, it maintains stable performance under complicated harsh working conditions.


The rollout of the UD18 series further enriches SICK’s ultrasonic sensor portfolio, turning it into a cost-effective preferred choice for global factories focusing on double-sheet detection.


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