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Description
7236 bcbm skfTechnische Basisdaten ueres Breitenma: 52 mm Ermdungsgrenzbelastung: 10 kN Gewicht: 17630 Gramm Grenzdrehzahl: 2 600 r min Grenzwert: e: 1. 14 Herstellungsland: sterreich Innendurchmesser: 180 mm Referenzdrehzahl: 2 400 r min Zolltarifnummer: 8482 1090 Erweiterte technische Daten 131 mm Abstand zwischen Stirnseite und Druckpunkt 52 mm ueres Breitenma Axiallastfaktor (Einzel Tandem): Y0: 0. 26 Axiallastfaktor (Einzel Tandem): Y2: 0. 57 Dynamische
Technische Basisdaten
| Äußeres Breitenmaß: 52 mm |
| Ermüdungsgrenzbelastung: 10 kN |
| Gewicht: 17630 Gramm |
| Grenzdrehzahl: 2 600 r/min |
| Grenzwert: e: 1.14 |
| Herstellungsland: Österreich |
| Innendurchmesser: 180 mm |
| Referenzdrehzahl: 2 400 r/min |
| Zolltarifnummer: 8482 1090 |
Erweiterte technische Daten
| 131 mm Abstand zwischen Stirnseite und Druckpunkt |
| 52 mm Äußeres Breitenmaß |
| Axiallastfaktor (Einzel Tandem): Y0: 0.26 |
| Axiallastfaktor (Einzel Tandem): Y2: 0.57 |
| Dynamische Tragzahl: 291 kN |
| Minimaler Axiallastfaktor: 2.21 |
| Minimaler Radialbelastungsfaktor: kr0.08 |
| Radiallastfaktor (Einzel Tandem): X: 0.35 |
| Statische Tragzahl: 375 kN |
| max.1.5 mm Radius der Rundung |
| max.3 mm Radius der Rundung |
| min.1.5 mm Kantenabstand |
| min.197 mm Durchmesser der WelleKeine/ Nicht vorhandenlauffläche |
| min.4 mm Kantenabstand |
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4.1 ★★★★★
Based on 16 reviews
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Product Reviews
★★★★★ 5
Before GPT can chat, it has to learn.
Format: Paperback
One of the most comprehensive guides to AI and deep learning available. All concepts covered clearly and concisely with illustrations. Complex concepts are broken down into understandable terms. Even though ML and AI require some complex math, you won’t need it to get idea of what’s going on inside the computer’s “brain”.
I use this as a reference when teaching AI concepts and preparing presentations.
I highly recommend it
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Reviewed in the United States on April 5, 2025
★★★★★ 5
Great examples and practical explanations
Format: Paperback
Excellent book. Highly recommend
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Reviewed in the United States on May 19, 2025
★★★★★ 5
Great for intuitive understanding
Format: Paperback
Amazing book. Great examples and diagrams. If you're looking to get an intuitive grasp of deep learning, look no further. If you're an engineer looking to apply it, I would recommend pairing this with one of the more technical canonical texts and a programming focused book.
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Reviewed in the United States on May 4, 2024
★★★★★ 5
Taught me backpropagation
Format: Paperback
I'm finding this book a great reference to supplement Syracuse IST 691 Deep Learning in Practice. In particular, its explanation of backpropagation is the clearest I have found yet, and that even includes 3 Blue 1 Brown, which always does excellent explanations.
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Reviewed in the United States on April 13, 2025
★★★★★ 5
An Easy Read
Format: Paperback
A good narrative description of how the various systems work. Good for getting a conceptual understanding. No math to speak of, and if you want it, you can refer to the references cited, or your other favorite machine learning book.
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Reviewed in the United States on May 7, 2024