Abstract
The design of rolling bearings has been a challenging task in the field of mechanical engineering. While most of the real aspects of the design are never disclosed by bearing manufacturers, the common engineer is left with no other alternative than to refer to standard tables and chart containing the bearing performance characteristics. This paper presents a more viable method to solve this problem using genetic algorithms (GAs). Since the algorithm is basically a guided random search, it weakens the chances of getting trapped in local maxima or minima. The method used has yielded improved performance parameters than those catalogued in standard tables.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 649-659 |
| Number of pages | 11 |
| Journal | Engineering Optimization |
| Volume | 35 |
| Issue number | 6 |
| DOIs | |
| State | Published - Dec 1 2003 |
| Externally published | Yes |
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Keywords
- Genetic algorithms
- Optimum mechanical design
- Rolling element bearings
ASJC Scopus subject areas
- Computer Science Applications
- Management Science and Operations Research
- Control and Optimization
- Industrial and Manufacturing Engineering
- Applied Mathematics
Cite this
Rolling element bearing design through genetic algorithms. / Chakraborty, Indraneel; Kumar, Vinay; Nair, Shivashankar B.; Tiwari, Rajiv.
In: Engineering Optimization, Vol. 35, No. 6, 01.12.2003, p. 649-659.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Rolling element bearing design through genetic algorithms
AU - Chakraborty, Indraneel
AU - Kumar, Vinay
AU - Nair, Shivashankar B.
AU - Tiwari, Rajiv
PY - 2003/12/1
Y1 - 2003/12/1
N2 - The design of rolling bearings has been a challenging task in the field of mechanical engineering. While most of the real aspects of the design are never disclosed by bearing manufacturers, the common engineer is left with no other alternative than to refer to standard tables and chart containing the bearing performance characteristics. This paper presents a more viable method to solve this problem using genetic algorithms (GAs). Since the algorithm is basically a guided random search, it weakens the chances of getting trapped in local maxima or minima. The method used has yielded improved performance parameters than those catalogued in standard tables.
AB - The design of rolling bearings has been a challenging task in the field of mechanical engineering. While most of the real aspects of the design are never disclosed by bearing manufacturers, the common engineer is left with no other alternative than to refer to standard tables and chart containing the bearing performance characteristics. This paper presents a more viable method to solve this problem using genetic algorithms (GAs). Since the algorithm is basically a guided random search, it weakens the chances of getting trapped in local maxima or minima. The method used has yielded improved performance parameters than those catalogued in standard tables.
KW - Genetic algorithms
KW - Optimum mechanical design
KW - Rolling element bearings
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UR - http://www.scopus.com/inward/citedby.url?scp=0345873395&partnerID=8YFLogxK
U2 - 10.1080/03052150310001624403
DO - 10.1080/03052150310001624403
M3 - Article
AN - SCOPUS:0345873395
VL - 35
SP - 649
EP - 659
JO - Engineering Optimization
JF - Engineering Optimization
SN - 0305-215X
IS - 6
ER -