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Description
6216NR NTNProduktdaten Marke NTN SNR d Innendurchmesser 80 mm a max Maximale Position der Nut 4,9 mm rs Minimaler Rundungsradius 2 mm rNs Minimaler Rundungsradius 0,5 mm b min Min Nutueres Breitenma 3,1 mm b max Maximale ueres Breitenma der Nut 3,4 mm r0 max Maximaler Radius der Sprengringnut 0,6 mm D4 max Maximaler Auendurchmesser bei montiertem Sprengring 149,7 mm f Dicke des Sprengrings 2,82 mm Referenz des Aueneringes R140 Radiallagerluftklasse CN Masse 1,4
Produktdaten
| Marke | NTN/SNR |
| d - Innendurchmesser | 80 mm |
| a max - Maximale Position der Nut | 4,9 mm |
| rs - Minimaler Rundungsradius | 2 mm |
| rNs - Minimaler Rundungsradius | 0,5 mm |
| b min - Min-NutÄußeres Breitenmaß | 3,1 mm |
| b max - Maximale Äußeres Breitenmaß der Nut | 3,4 mm |
| r0 max - Maximaler Radius der Sprengringnut | 0,6 mm |
| D4 max - Maximaler Außendurchmesser bei montiertem Sprengring | 149,7 mm |
| f - Dicke des Sprengrings | 2,82 mm |
| Referenz des Außeneringes | R140 |
| Radiallagerluftklasse | CN |
| Masse | 1,4 kg |
| Leistungsdaten | |
|---|---|
| C - Dynamische Tragzahl | 80,5 kN |
| C0 - Statische Tragzahl | 53 kN |
| Cu - Ermüdungsgrenzbelastung | 3,95 kN |
| f0 - Koeffizient | 14.6 |
| Tmin - Min Betriebstemperatur | -40 °C |
| Tmax - Max Betriebstemperatur | 120 °C |
| DEFINITIONSEMPFEHLUNGEN DER UMGEBUNGSTEILE | |
|---|---|
| da min - Minimaler Schulterdurchmesser IR | 89 mm |
| Da max - Maximaler Schulterdurchmesser AR | 131 mm |
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4.3 ★★★★★
Based on 25 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