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Description
23132 e1a xl k m fagTechnische Basisdaten Hersteller: FAG Abdichtung: beidseitig Befestigungshlse: AH3132A Freiraumueres Breitenmae: B a min 8 mm Minimale fr Hlse Bezugsdrehzahl: 1. 560 1 min ueres Breitenma: 86 mm Gewicht: 19100 Gramm Grenzdrehzahl: 2. 490 1 min Herstellungsland: Deutschland Innendurchmesser: 160 mm Zolltarifnummer: 8482 3000 Erweiterte technische Daten A = Verbesserte Innenkonstruktion ueres Breitenmae: mm 270,00 ueres Breitenmae: mm 86,00 E1 Increased
Technische Basisdaten
| Hersteller: FAG |
| Abdichtung: beidseitig |
| Befestigungshülse: AH3132A |
| FreiraumÄußeres Breitenmaße: B a min 8 mm Minimale für Hülse |
| Bezugsdrehzahl: 1.560 1/min |
| Äußeres Breitenmaß: 86 mm |
| Gewicht: 19100 Gramm |
| Grenzdrehzahl: 2.490 1/min |
| Herstellungsland: Deutschland |
| Innendurchmesser: 160 mm |
| Zolltarifnummer: 8482 3000 |
Erweiterte technische Daten
| A = Verbesserte Innenkonstruktion |
| Äußeres Breitenmaße: mm 270,00 |
| Äußeres Breitenmaße: mm 86,00 |
| E1 Increased Capacity Design |
| Einzelgewicht in (kg): 19.1 |
| Innenmaße: mm 160,00 |
| Interne Radialluft Cn Normale Innenluft |
| M Solid Brass Cage, Guided By Rollers |
| Schwingsiebausführung Keine Schwingsiebausführung |
| AH3132A Befestigungshülse |
| B 86 mm Äußeres Breitenmaße: |
| Betriebstemperatur max.: 200 °C |
| Betriebstemperatur min.: -30 °C |
| C 0r 1.550.000 N Statische Tragzahl, radial |
| C r 1.160.000 N Dynamische Tragzahl, radial |
| C ur 166.000 N Ermüdungsgrenzbelastung, radial |
| Durchmesser Schmierkanal: 8 mm |
| Dynamische Tragzahl, radial: 1.160.000 N |
| Dynamischer Axiallastfaktor: 2,32 |
| Dynamischer Axiallastfaktor: 3,45 |
| Ermüdungsgrenzbelastung, radial: 166.000 N |
| Grenzwert für Fa/Fr für die Anwendbarkeit der versch. Werte der Faktoren X und Y: 0,29 |
| Maximaler Durchmesser der Wellenschulter: 183 mm |
| Maximaler Freistichradius: 2,1 mm |
| Minimale FreiraumÄußeres Breitenmaß für Hülse: 8 mm |
| Minimaler Anlagedurchmesser Wellenschulter: 172 mm |
| Minimaler Kantenabstand: 2,1 mm |
| Statische Tragzahl, radial: 1.550.000 N |
| Statischer Axiallastfaktor: 2,26 |
| T max 200 °C Betriebstemperatur max. |
| T min -30 °C Betriebstemperatur min. |
| Y 0 2,26 Statischer Axiallastfaktor |
| Y 1 2,32 Dynamischer Axiallastfaktor |
| Y 2 3,45 Dynamischer Axiallastfaktor |
| d =a max 183 mm Maximaler Durchmesser der Wellenschulter |
| d =a min 172 mm Minimaler Anlagedurchmesser Wellenschulter |
| d =s 8 mm Durchmesser Schmierkanal |
| e 0,29 Grenzwert für Fa/Fr für die Anwendbarkeit der versch. Werte der Faktoren X und Y |
| n G 2.490 1/min Grenzdrehzahl |
| n ϑr 1.560 1/min Bezugsdrehzahl |
| r a max 2,1 mm Maximaler Freistichradius |
| r min 2,1 mm Minimaler Kantenabstand |
| ≈m 19,267 kg Einzelgewicht |
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4.5 ★★★★★
Based on 88 reviews
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Product Reviews
★★★★★ 5
Great book
Format: Paperback
Great book! It is helpful as I work towards my Masters of Science in Applied Artificial Intelligence
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Reviewed in the United States on December 20, 2025
★★★★★ 5
Comprehensive Guide with Practical Insights
Format: Paperback
This is a solid book for understanding the art and science of working with LLMs and other generative AI models. I always struggled with getting the output I was looking for, and wasn't sure how best to "ask" the models for what I wanted. This book did a great job of laying out the strategies and practical guidance to craft the prompts. There were a lot of tips and tricks, but the overall understanding and framework around prompt engineering has been super useful.
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Reviewed in the United States on June 25, 2024
★★★★★ 5
A must read!
Format: Paperback
Hands down, the best book on prompt engineering and implementing LLMs. Really enjoyed Michael and James deep dive. Whether you're technical or not, this book is foundational to a deeper understanding in how to properly explore and implement LLMs.
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Reviewed in the United States on June 25, 2024
★★★★★ 4
Helpful for GPT/LangChain framework
Format: Paperback
I applaud the authors for putting forth a comprehensive introduction in a rapidly evolving space. I absorbed a lot, helpful as I was developing a prototype — using open source methods.
And that’s where I was disappointed. The LLM and examples are highly adapted to OpenAI’s GPT-x and the bulky LangChain framework, something not obvious until you dig in to the book. Sure, this may be where newbie demand was when the authors began writing. But as the open source models and OpenAI alternatives gain speed (e.g. Llama 3.1, Groq, etc.) this book may quickly need an updated and expanded version to stay relevant.
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Reviewed in the United States on August 7, 2024
★★★★★ 3
Good concepts, but uneven depth
Format: Paperback
Some helpful prompt frameworks, but parts felt repetitive and a few sections stayed too high-level for advanced users. Beginners will likely benefit more than experienced practitioners. If you’re new to prompt engineering it’s a decent starting point; if you’re already building structured prompts daily, it may feel light.
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Reviewed in the United States on January 17, 2026