A genetic algorithm approach to minimize transmission error of automotive spur gear sets

D. J. Fonseca, S. Shishoo, T. C. Lim, D. S. Chen

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

As the quietness of vehicles has improved in recent years, there have been stricter requirements to reduce gear vibration and noise and thereby improve transmission quality. The prediction of gear vibration and noise has always been a major concern in gear design. It is widely accepted that the acoustic noise generated by a pair of gears is strongly related to the gears transmission error. Recently, greater emphasis has been placed to further optimize the gear tooth parameters in order to reduce transmission error and, subsequently, improve the dynamic performance of transmission systems. This paper discusses the development of a genetic algorithm (GA) model to minimize the weighted sum of the magnitudes of gear mesh frequency components. The GA algorithm was designed based on a mathematical formulation for computing static transmission error and load sharing for low-contact-ratio external spur gears. The constructed GA was able to overcome the local optima and achieve global optimal/near-optimal solutions.

Original languageEnglish (US)
Pages (from-to)153-179
Number of pages27
JournalApplied Artificial Intelligence
Volume19
Issue number2
DOIs
StatePublished - Feb 2005
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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