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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/93681

    Title: 圓柱齒輪感應硬化特徵預測之研究;A Study on Feature Prediction of Cylindrical Gear Induction Hardening
    Authors: 黃子賢;Ng, Zhu-Heang
    Contributors: 機械工程學系
    Keywords: 齒輪感應硬化熱處理;齒輪特徵預測;均勻實驗設計法;有 限元素法;徑向基函數;粒子群最佳化;Gear induction hardening;Gear feature prediction;Uniform design experiment method;Finite element analysis;Radial basis functions;Particle swarm optimization
    Date: 2024-01-29
    Issue Date: 2024-03-05 18:00:55 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 齒輪製造工序一般從鋼胚加工出齒型,接著進行感應硬化熱處理
    本研究提出預測模型較適用於預測齒輪感應硬化熱處理特徵之工具。;Gear manufacturing processes typically begin with shaping the teeth from a steel blank, followed by induction hardening to enhance tooth strength. Subsequently, precision machining is performed to achieve gear accuracy. In response to energy conservation and carbon reduction, the final precision machining step is omitted, simplifying the gear
    manufacturing process. To achieve this goal, predicting the characteristics of gears after induction hardening becomes a crucial tool. This study proposes the application of uniform experimental design for experimental planning of cylindrical gear induction hardening. A predictive model is established using radial basis functions to anticipate gear characteristics after induction hardening, with the derivation of respective target prediction equations. Additionally, finite element analysis is employed to simulate gear induction hardening, advocating the use of a non-uniform magnetic field model for enhanced accuracy. Subsequently, particle swarm optimization is utilized to determine optimal heating control factors for specific gear features. Finally, applying the optimal solution to finite
    element simulation produces results for the non-uniform magnetic field model, validated against predictive model outcomes. A comparison between non-uniform and uniform magnetic field models and predictive model results validates the necessity of employing a non-uniform magnetic field model. In conclusion, this study proposes that the predictive model is more suitable for forecasting characteristics in the induction.
    Appears in Collections:[機械工程研究所] 博碩士論文

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