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Description
NN3021 KTN9/SP SKFTechnische Basisdaten Hersteller: SKF Auendurchmesser: 160 ueres Breitenma: 41 Ermdungsgrenzbelastung: 36 kN Fettbezugsmenge: Gref: 17: cm Gewicht: 2810 Gramm Erweiterte technische Daten ueres Breitenmae:(D) mm 160,00 ueres Breitenmae:(B) mm 41,00 Einzelgewicht in (kg): 2. 96 Innenmae:(d) mm 105,00 Interne Radialluft Cn Normale Innenluft Przision SP Precision Higher than P4S SP Special Precision Class For Machine Tool Bearings: Dimensional Accuracy
Technische Basisdaten
| Hersteller: SKF |
| Außendurchmesser: 160 |
| Äußeres Breitenmaß: 41 |
| Ermüdungsgrenzbelastung: 36 kN |
| Fettbezugsmenge: Gref: 17: cm³ |
| Gewicht: 2810 Gramm |
Erweiterte technische Daten
| Äußeres Breitenmaße:(D) mm 160,00 |
| Äußeres Breitenmaße:(B) mm 41,00 |
| Einzelgewicht in (kg): 2.96 |
| Innenmaße:(d) mm 105,00 |
| Interne Radialluft Cn Normale Innenluft |
| Präzision SP - Precision Higher than P4S |
| SP Special Precision Class For Machine Tool Bearings: Dimensional Accuracy Approximately To P5, Running Accuracy Approximately To P4 |
| TN9 Injection Moulded Cage Of Glass Fibre Reinforced Polyamide 6,6 |
| Wärmestabilisierung: Keine Wärmestabilisierung: - Temperaturen bis 120°C |
| 160 mm Außendurchmesser |
| 41 mm Äußeres Breitenmaß |
| Anzahl der Reihen 2 |
| Außendurchmesser 160 mm |
| Beschichtung Ohne |
| Äußeres Breitenmaß 41 mm |
| Dynamische Tragzahl 190 kN |
| Dynamische Tragzahl: 190 kN |
| Einzelgewicht 2.66 kg |
| Erreichbare Drehzahlen für Öl-Luft-Lubrikation: 6 000 r/min |
| Loser Bord Kein(e/r) |
| Nachschmierfunktion Ohne |
| Statische Tragzahl 305 kN |
| Statische Tragzahl: 305 kN |
| Statische radiale Steifigkeit (Richtwert): 2 330: N/µm |
| UNSPSC-Code 31171505 |
| eClass-Code 23-05-09-01 |
| max.2 mm Zulässige axiale Verschiebbarkeit von der normalen Position eines Rings gegenüber dem anderen Ring (alle) |
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4.6 ★★★★★
Based on 10 reviews
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Product Reviews
★★★★★ 5
Upside down (Great)
Format: Hardcover
Was extremely relatable and a blessing I gifted this book and want it back lol but I will be ordering again
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on October 23, 2024
★★★★★ 5
Good book
Format: Paperback
Still working through it but I have no complaints. I have a shelf of no starch books and have not been disappointed by any. Some, of course, are better than others but this is a good book.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 2, 2021
★★★★★ 5
Dive into Algorithms is more like eating a Parfait than trying understand algorithms!
Format: Paperback
Seriously though great writing and explanation along with great history lessons. Also highly recommend for anyone working in the data science field.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on August 3, 2021
★★★★★ 3
Ok content, not great explanations (2.5 our of 5 stars)
Format: Paperback
I found the contents of this book to be simply ok: either too simple, or the more complicated algorithms and concepts would not be as carefully explained as they should have been. It is not a terrible book, but it feels as though the author did not go over many drafts/iterations of this work.
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Reviewed in the United States on April 7, 2024
★★★★★ 5
An Excellent Self-Discovery Approach to Learning Algorithms
Format: Paperback
I have an Ivy League Master's Degree in Computer Science although it was accomplished 35 years ago. Of course, I had to complete an ACM-type course in Algorithms and Data Structures on the undergraduate and graduate level and managed to by rote accomplish enough to satisfy these courses. But until seeing this great book I never had the feeling of gaining an understanding of the approach to learning and building algorithms and the extent to which it is an important component of all programs. By a journey of guided self-discovery the author shows, not only the necessity of algorithms and their canonical forms, but a path to understanding the construction of algorithms to accomplish common and not so common practical problems. These range from the simple to understand, e.g. implementing Russian Peasant Multiplication, to advanced and up to date topics like Machine Learning.
The highest praise I can give this book is that as a journey of guided self-discovery it produces an understanding in the reader of the process of constructing and understanding these algorithms and their place in all programming.
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Reviewed in the United States on February 7, 2021
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