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  • 學位論文

精密磨床熱變形分析與預測

Analysis and Prediction of Thermal Deformation for Precision Grinding Machine

指導教授 : 康 淵
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摘要


本文使用有限元素法,以規律與隨機兩種不同之熱源型式,在主軸、立柱滑軌、工作台滑軌、鞍座滑軌面施予熱通量進行分析,模擬機台在單方向位置與多方向位置受到熱源影響時之瞬態溫度及熱變形的變化,求得主軸端點、主軸座上端中間位置、工作台與鞍座滑軌接觸面、立柱與主軸座滑軌接觸面及鞍座與底座滑軌接觸面之溫度探討熱源對機台之影響。 採用前向式類神經網路,利用分析之溫度及熱變形結果,以溫度為網路之輸入,網路之輸出為熱變形預測值,建立熱變形預測模型,於分析結果中分別加上0.5%、1%、2%、5%、10%、15%、20%、25%與 30%的白雜訊(White noise),模擬於現場溫度量測時,感測器受到雜訊干擾,分析不同雜訊對預測模型之精度影響,探討前向是神經網路在不同雜訊影響下之預測精度。 作為工具機熱變形預測模型設計之參考,如此不僅可縮減工具機熱補償硬體改善成本,並可提昇工具機之加工精度增加工具機市場的競爭力。

並列摘要


This study uses the heat sources of regulation and randen types to simulate thermal deformations and temperatures from one direction and multiple directions by finite element method. Using the analyzed results of finite element method to build the prediction model which by feed-forward Neural Network input as temperatures and output as thermal deformations. After modeling, the other analyzed results add with white noise which are 0.5%, 1%, 2%, 5%, 10%, 15%, 20%, 25% and 30% for the original temperatures, to discuss the predicted accuracy of Neural Network.

參考文獻


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被引用紀錄


陳冠憲(2016)。平面磨床加工參數探討〔碩士論文,逢甲大學〕。華藝線上圖書館。https://doi.org/10.6341/fcu.M0359937

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