Rolling element bearing design through genetic algorithms

Indraneel Chakraborty, Vinay Kumar, Shivashankar B. Nair, Rajiv Tiwari

Research output: Contribution to journalArticle

52 Citations (Scopus)

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 languageEnglish (US)
Pages (from-to)649-659
Number of pages11
JournalEngineering Optimization
Volume35
Issue number6
DOIs
StatePublished - Dec 1 2003
Externally publishedYes

Fingerprint

Bearings (structural)
Tables
Genetic algorithms
Genetic Algorithm
Rolling Bearing
Random Search
Chart
Mechanical engineering
Engineering
Alternatives
Engineers
Design
Standards
Genetic algorithm

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 journalArticle

Chakraborty, Indraneel ; Kumar, Vinay ; Nair, Shivashankar B. ; Tiwari, Rajiv. / Rolling element bearing design through genetic algorithms. In: Engineering Optimization. 2003 ; Vol. 35, No. 6. pp. 649-659.
@article{ed6c5df148594bc58e5a60e3ffa8fbfb,
title = "Rolling element bearing design through genetic algorithms",
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.",
keywords = "Genetic algorithms, Optimum mechanical design, Rolling element bearings",
author = "Indraneel Chakraborty and Vinay Kumar and Nair, {Shivashankar B.} and Rajiv Tiwari",
year = "2003",
month = "12",
day = "1",
doi = "10.1080/03052150310001624403",
language = "English (US)",
volume = "35",
pages = "649--659",
journal = "Engineering Optimization",
issn = "0305-215X",
publisher = "Taylor and Francis Ltd.",
number = "6",

}

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

UR - http://www.scopus.com/inward/record.url?scp=0345873395&partnerID=8YFLogxK

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 -