Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications

Reliability Optimum Design for Bevel Gear Driven Systems Based on Genetics Algorithm

Authors
Chang Li, Mingyong Hu, Xing Han
Corresponding Author
Chang Li
Available Online November 2016.
DOI
https://doi.org/10.2991/aiea-16.2016.66How to use a DOI?
Keywords
Bevel gear driven systems; reliability optimum design; random parameters; genetics algorithm.
Abstract

Genetics algorithm is a global group search optimum calculation method based on the natural selections and the genetic variations. Mechanic reliability design makes sure that the reliability indexes can be realized. However, it cannot guarantee that the products have the best working performances and the optimum design parameters. In this paper, genetics algorithm is introduced into the reliability optimum design for bevel gear driven systems, and then the optimum design parameters of driven systems are obtained under the allowable reliability degree. This method can quantificational make optimum analysis for bevel gear driven systems, and is very useful for the engineering application.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications
Series
Advances in Computer Science Research
Publication Date
November 2016
ISBN
978-94-6252-270-1
ISSN
2352-538X
DOI
https://doi.org/10.2991/aiea-16.2016.66How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Chang Li
AU  - Mingyong Hu
AU  - Xing Han
PY  - 2016/11
DA  - 2016/11
TI  - Reliability Optimum Design for Bevel Gear Driven Systems Based on Genetics Algorithm
BT  - Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications
PB  - Atlantis Press
SP  - 369
EP  - 374
SN  - 2352-538X
UR  - https://doi.org/10.2991/aiea-16.2016.66
DO  - https://doi.org/10.2991/aiea-16.2016.66
ID  - Li2016/11
ER  -