Title page for etd-0717123-131659


URN etd-0717123-131659 Statistics This thesis had been viewed 36 times. Download 4 times.
Author Jun-Ting Lin
Author's Email Address juntinglin627@gmail.com
Department Institute Of Mechanical Engineering
Year 2022 Semester 2
Degree Master Type of Document Master's Thesis
Language zh-TW.Big5 Chinese Page Count 57
Title Finite Element Analysis and Optimum Design of Aluminum Wheels for Electric Scooters
Keyword
  • weight minimization
  • optimum design
  • finite element analysis
  • reverse engineering
  • wheel
  • wheel
  • reverse engineering
  • finite element analysis
  • optimum design
  • weight minimization
  • Abstract Nowadays, as electric scooters getting more popular, the safety and
    efficiency of the vehicles have become important research topics. To
    improve the efficiency of the electric scooters, their parts are redesigned
    to reduce the weight and the wheels are often chosen as the subjects for
    weight reduction resign.
    Based on a commercially available aluminum wheel for an electric
    scooter, this research utilized a 3D scan technology to acquire a set of
    precise dimensions of the wheel, employed Creo Parametric software to
    build solid models, and used Ansys Workbench to perform finite element
    analysis and structural optimization on the wheel. Convergence analysis
    was first conducted to yield a reduced model with appropriate element
    sizes and finite element grids for later use in optimization processes.
    Subsequently, wheel mass minimizations were investigated. The wheel
    mass was set as the objective function, the upper limit of the maximum
    equivalent stress and the lower limit of the first natural frequency of the
    model were selected as the design constraints and based on these
    constraints, three types of optimum design problems were defined. Three
    different optimization algorithms were employed and the results show that
    the mass of the aluminum wheel was reduced to 3.2363 kg, 3.2584 kg and
    3.2582 kg, respectively, for the three types of optimum design problems,
    a significant improvement for all three.
    Advisor Committee
  • Kun-Nan Chen - advisor
  • Wei-Hsin Gau - co-chair
  • Wen-Der Ueng - co-chair
  • Files indicate access worldwide
    Date of Defense 2023-07-05 Date of Submission 2023-07-17

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