A Comparison of Spindle Ball Bearings with Steel or Ceramic Balls for Very High Speed Applications

2009 ◽  
pp. 121-121-13
Author(s):  
K Buchner
Keyword(s):  
High Speed ◽  
Ball Bearings ◽  
Very High

Structural changes in silicon-on-insulator material during post-implantation annealing

1986 ◽  
Vol 44 ◽  
pp. 736-737
Author(s):  
C. O. Jung ◽  
S. J. Krause ◽  
S.R. Wilson
Keyword(s):  
Integrated Circuits ◽  
Oxide Layer ◽  
High Speed ◽  
Structural Changes ◽  
Device Fabrication ◽  
Silicon On Insulator ◽  
High Quality ◽  
Radiation Hardened ◽  
Post Implantation ◽  
Very High

Silicon-on-insulator (SOI) structures have excellent potential for future use in radiation hardened and high speed integrated circuits. For device fabrication in SOI material a high quality superficial Si layer above a buried oxide layer is required. Recently, Celler et al. reported that post-implantation annealing of oxygen implanted SOI at very high temperatures would eliminate virtually all defects and precipiates in the superficial Si layer. In this work we are reporting on the effect of three different post implantation annealing cycles on the structure of oxygen implanted SOI samples which were implanted under the same conditions.

YSS HAP72

Alloy Digest ◽  
2019 ◽  
Vol 68 (10) ◽  
Keyword(s):  
Wear Resistance ◽  
High Temperature ◽  
Powder Metallurgy ◽  
Tool Steel ◽  
High Speed ◽  
High Wear Resistance ◽  
Bend Strength ◽  
Temperature Performance ◽  
Very High

Abstract YSS HAP72 is a powder metallurgy high-speed tool steel with a very high wear resistance. This datasheet provides information on composition, hardness, and bend strength. It also includes information on high temperature performance. Filing Code: TS-779. Producer or source: Hitachi Metals America Ltd.

Very High-Speed Report File System

1992 ◽  
Author(s):  
Timothy J. Salo ◽  
John D. Cavanaugh ◽  
Michael K. Spengler
Keyword(s):  
High Speed ◽  
File System ◽  
Very High

Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

2019 ◽  
Vol 12 (3) ◽  
pp. 248-261
Author(s):  
Baomin Wang ◽  
Xiao Chang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Temperature Rise ◽  
High Speed ◽  
Ball Bearing ◽  
Influence Factors ◽  
Ball Bearings ◽  
Ann Model ◽  
Angular Contact Ball Bearing ◽  
Artificial Neural

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

Very high speed continuous sampling using matched delays

1994 ◽  
Vol 30 (6) ◽  
pp. 463-465 ◽  
Author(s):  
S.M. Clements ◽  
R.K. Cavin III ◽  
J. Kang ◽  
W. Liu
Keyword(s):  
High Speed ◽  
Continuous Sampling ◽  
Very High
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