SKU: 25981132912

XS8D1A1PAM12

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Description

XS8D1A1PAM12Main Range of product OsiSense XS Series name General purpose Sensor type Inductive proximity sensor Device application Sensor name XS8 Sensor design Flat form 80 x 80 x 26 Size 26 mm Body type Fixed Detector flush mounting acceptance Non flush mountable Material Plastic Enclosure material PBT Type of output signal Discrete Wiring technique 3 wire [Sn] nominal sensing distance 40 mm flush mountable 60 mm non flush mountable Discrete output function 1

Main
Range of product OsiSense XS
Series name General purpose
Sensor type Inductive proximity sensor
Device application -
Sensor name XS8
Sensor design Flat form 80 x 80 x 26
Size 26 mm
Body type Fixed
Detector flush mounting acceptance Non flush mountable
Material Plastic
Enclosure material PBT
Type of output signal Discrete
Wiring technique 3-wire
[Sn] nominal sensing distance 40 mm flush mountable
60 mm non flush mountable
Discrete output function 1 NO
Output circuit type DC
Discrete output type PNP
Electrical connection Male connector M12, 4 pins
[Us] rated supply voltage 12...24 V DC with reverse polarity protection
Switching capacity in mA <= 200 mA DC with overload and short-circuit protection
IP degree of protection IP67 double insulation conforming to IEC 60529
Complementary
Detection face Frontal
Front material PBT
Fine adjustment zone 20…40 mm flush mountable
20…60 mm non flush mountable
Differential travel 1...15% of Sr
Supply voltage limits 10…36 V DC
Maximum residual current 1.5 mA open state
Switching frequency <= 150 Hz
Maximum voltage drop <2 V (closed)
Current consumption <= 10 mA no-load
Maximum delay first up 10 ms
Maximum delay response 0.3 ms
Maximum delay recovery 6 ms
Marking CE
Depth 26 mm
Height 80 mm
Width 80 mm
Net weight 0.34 kg
Environment
Product certifications CSA
UL
Ecolab
Ambient air temperature for operation -25…70 °C
Ambient air temperature for storage -40…85 °C
Vibration resistance 25 gn amplitude = +/- 2 mm (f = 10…55 Hz) conforming to IEC 60068-2-6
Shock resistance 50 gn for 11 ms conforming to IEC 60068-2-27
Offer Sustainability
Sustainable offer status Green Premium product
REACh Regulation
REACh Declaration
EU RoHS Directive Pro-active compliance (Product out of EU RoHS legal scope)
EU RoHS Declaration
Mercury free Yes
RoHS exemption information
Yes
Environmental Disclosure
Product Environmental Profile
Circularity Profile No need of specific recycling operations
End of Life Information
Contractual warranty
Warranty 18 months
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SKU: 25981132912

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Abdul
Phoenix, US
★★★★★ 5
Great primer for building LLM Applications
Format: Kindle
I highly recommend the book for anyone who wants to come up to speed on running Ollama for open models. The book is well structure and teach you how to create a working RAG Application. I recommend as a primer for anyone interested in building LLM Applications.
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Y. Banga
Chelsea, US
★★★★★ 5
Breaks down complex concepts into bite-sized, practical chapters.
Format: Kindle
This hands-on guide to Ollama is exactly what I needed! The author breaks down complex concepts into bite-sized, practical chapters that get straight to the point. The sections on different Ollama models and customization were particularly helpful. If you're looking to learn about building local LLM-powered applications without any fluff, this book is a great investment.
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William L. Crupi
Omaha, US
★★★★★ 5
Great helpful book.
Format: Kindle
This book is simple to follow, Greg gets back to you in a very timely manner. I have bought other books from Greg and they are always very helpful.
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jeff s kittrell
Alexandria, US
★★★★★ 5
Love it - revolutionary approach to teaching, presenting complex concepts
Format: Paperback
This book is a game-changer for neural networks and AI enthusiasts. It takes a revolutionary approach to teaching, presenting complex concepts in a simple and engaging manner. The book is filled with colorful diagrams and illustrations that make learning accessible and enjoyable. I’ve read several other books, but this one stands out. It’s not a beginner’s guide, but once you grasp the early concepts, it takes you to a whole new level of understanding. I will give it 10 stars if I could.
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Reviewed in the United States on June 18, 2025
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Amazon User
Dallas, US
★★★★★ 5
Great book to understand Neural Networks!
Format: Paperback
Loved the book and I highly recommend. It was thorough and very easy to understand.
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Reviewed in the United States on December 1, 2025

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