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A computation strategy based on neural network for stiffness determination of deep‐groove ball bearings

A computation strategy based on neural network for stiffness determination of deep‐groove ball... All deep‐groove ball bearings have similar features in geometry, mechanism, and structure. Stiffness of this type of bearings is related to geometry, dimensions, and operating conditions by a very complex, high‐order and coupled‐variable function. This paper has verified that the stiffness function for all deep‐groove ball bearings can be replaced by a back‐propagation neural network (BPNN) which is trained by using some (not all) samples. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Industrial Lubrication and Tribology Emerald Publishing

A computation strategy based on neural network for stiffness determination of deep‐groove ball bearings

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Publisher
Emerald Publishing
Copyright
Copyright © 2004 Emerald Group Publishing Limited. All rights reserved.
ISSN
0036-8792
DOI
10.1108/00368790410532183
Publisher site
See Article on Publisher Site

Abstract

All deep‐groove ball bearings have similar features in geometry, mechanism, and structure. Stiffness of this type of bearings is related to geometry, dimensions, and operating conditions by a very complex, high‐order and coupled‐variable function. This paper has verified that the stiffness function for all deep‐groove ball bearings can be replaced by a back‐propagation neural network (BPNN) which is trained by using some (not all) samples.

Journal

Industrial Lubrication and TribologyEmerald Publishing

Published: Jun 1, 2004

Keywords: Mechanical components; Neural nets; Computer aided design

References