Optimization of the gear ratios in automatic transmission systems using an artificial neural network and a genetic algorithm

One of the most important tasks in designing an automatic transmission system is to find the gear ratios and the corresponding number of gear teeth. In this paper, an artificial neural network and a genetic algorithm are used for this optimization with regard to an epicyclic gear train. First, MATLAB and an artificial neural network are employed to model the system, and the results depict the accuracy of the artificial neural network calculations. Then, using the same software and with the aid of a genetic algorithm, the optimized speed ratios and gear ratios are obtained. It can be seen that a series of gear ratios is produced. Another genetic algorithm was used to calculate the optimized gear ratio and the corresponding number of gear teeth. A Simpson gear train is used to demonstrate the methodology. The proposed model is very accurate and efficient, such that the resulting numbers of optimized gears have an error of less than ±0.3%. This method is much easier and has a lower computation cost than solving the related equations.

• Record URL:
  https://doi.org/10.1177/0954407014528887
• Record URL:
  http://pid.sagepub.com/cgi/content/abstract/228/11/1338
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09544070
• Supplemental Notes:
  • Reprinted by permission of Sage Publications, Ltd.
• Authors:
  • Shamekhi, Amir H
  • Bidgoly, Abbas
  • Noureiny, Ebrahim N
• Publication Date: 2014-9

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 1338-1343
• Serial:
  • Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
  • Volume: 228
  • Issue Number: 11
  • Publisher: Sage Publications Limited
  • ISSN: 0954-4070
  • EISSN: 2041-2991
  • Serial URL: http://pid.sagepub.com/content/current

Subject/Index Terms

• TRT Terms: Automatic transmissions; Gear teeth; Gears; Genetic algorithms; Neural networks; Optimization; Vehicle design
• Subject Areas: Highways; Vehicles and Equipment; I91: Vehicle Design and Safety;

Filing Info

• Accession Number: 01540050
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 9 2014 9:01AM