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Analytical study regarding the topological optimization of an automotive gear wheel pair

Author

Listed:
  • Mihai Gaidur
  • Ionut Pascal
  • Edward Rakosi
  • Tudor-Marian Ulian
  • Gheorghe Manolache

Abstract

This academic research explores the application of topology optimization to enhance the design of an automotive gearwheel pair. The study establishes boundary and load conditions, conducting finite element simulations to assess the impact on the gear pair's performance. The primary objective is to reduce component volume while maintaining the initial rigidity of the gear pair. The study uses scenario-based topology optimization to change the percentages of mass reductions and then looks at how these changes affect things like the safety factor, Von Mises stress, displacement, and equivalent strain. The study highlights the trade-offs between mass reduction and the gear pair's mechanical performance, emphasizing the need for a comprehensive risk/gain analysis in optimization decisions. Various scenarios are presented, with scenario II showing the most favorable outcomes, significantly improving all analyzed parameters for gear . Conversely, scenario V for gear exhibits a decline in all parameters, making it the least favorable scenario. The study underscores the potential benefits of topology optimization, such as achieving lightweight and durable gear designs, though it acknowledges computational intensity and time constraints. Ultimately, the authors recommend further exploration and optimization based on scenarios III and VI, which exhibit the highest relevance according to the study's results. The research contributes to the broader field of engineering design optimization, with implications for improved automotive transmission systems and drivetrain efficiency.

Suggested Citation

  • Mihai Gaidur & Ionut Pascal & Edward Rakosi & Tudor-Marian Ulian & Gheorghe Manolache, 2023. "Analytical study regarding the topological optimization of an automotive gear wheel pair," Edelweiss Applied Science and Technology, Learning Gate, vol. 7(1), pages 38-70.
  • Handle: RePEc:ajp:edwast:v:7:y:2023:i:1:p:38-70:id:333
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