臺灣博碩士論文加值系統

當工具機在加工過程中會受到室溫、還有許多因素所影響，使主軸、馬達、導螺桿等不同的元件溫度上升，則溫度上升各元件將產生熱變形，固本研究針對溫度和熱變形之間的關係進行探討。因此本文聚焦在「麥克機器實業有限公司」之球形工具機，搭配控制器為國產新代科技公司的SYNTEC21-TA型控制器，採用普森皮帶式主軸。分別以1000、1500、2000、2500、3000每分鐘轉速（Revolution Per Minute，RPM）之下，監控主軸的熱溫升和變化，藉由溫度感測器埋藏在前軸承和後軸承附近的溫度量測，同時也量測軸向的熱變位，使用了KEYENCE的雷射位移計再以NI的擷取卡進行資料擷取，則將收集的數據進行整理，再使用了迴歸神經網路進行進行預測，並與實際值進行比較，其 為0.95。此外，本研究使用COMSOL模擬分析軟體，並運用試誤學習調整邊界條件來模擬因主軸軸承運轉發熱對於主軸結構所產生的熱行為特性和軸向位移進行分析，其結果與實際量測比對，則溫升值誤差在1℃，位移值誤差在1 以內。

Many factors, including room temperature, can cause the spindle, motor, screws and other components to heat up as machine tools are used during the machining process. This increase in temperature can cause thermal deformation in the different parts. In this study we used a Mike Machine Industry spherical machine tool, a domestically produced SYNTEC21-TA controller and a POSA belt-driven spindle to test the temperature increase and axial displacement changes at 1000, 1500,2000, 2500, 3000 rpm. Temperature sensors embedded in the front and rear bearings are used to measure nearby temperature and the axial thermal displacement. A KEYENCE laser displacement meter and an NI capture card are used to extract data, which is collected and organized into an Excel format. The regression neural network is used for prediction and compared with the actual value, and the coefficient of determination is 0.95. In addition, this study used a COMSOL simulation analysis software and utilized try-and-error learning to adjust boundary conditions. Simulation of the effects of spindle bearing operations heating on the thermal behavior and axial displacement of the spindle structure was conducted and analyzed.
The result is compared with the actual measurement, the temperature rise value error is 1 ° C, and the displacement value error is within 1 .

摘要 I
Abstract II
目錄 V
表目錄 VII
圖目錄 VIII
第一章 序論 1
1.1 前言 1
1.2 文獻回顧 2
1.3 論文架構 6
第二章 研究系統架構與簡介 7
2.1 工具機熱變形 10
2.2 熱的傳遞方式 11
2.3 熱變形的型態 13
2.4 轉速與升溫的關聯性 15
2.4.1 軸承負荷計算 16
2.4.2 軸承發熱量 17
2.4.3 實例計算 20
第三章 方法理論介紹 21
3.1 迴歸神經網路（Generalized Regression Neural Network，GRNN） 21
3.1.1 迴歸神經網路理論 21
3.1.2 訓練GRNN的平滑參數 24
3.1.3 模型驗證 25
第四章 實驗結果與討論 26
4.1 溫度量測系統 26
4.1.1 溫度感測器介紹 26
4.1.2 溫度感測器選用 27
4.1.3 溫度擷取系統 28
4.1.4 溫度架設點位置 30
4.2 位移量測系統 31
4.2.1 位移計介紹 31
4.2.2 位移感測器選用 31
4.2.3 治具設計 35
4.2.4 位移信號擷取系統 37
4.2.5 人機介面 38
4.3 溫度及位移量測 39
4.4 COMSOL分析 42
4.4.1 主軸結構分析流程圖 42
4.4.2 主軸結構建模 43
4.4.3 設定材料性質 44
4.4.4 發熱量設定 45
4.4.5 對流係數設定 45
4.4.6 網格設定 45
4.4.7 有限元素分析 47
4.4.8 有限元素熱固耦合分析 49
4.5 迴歸神經網路預測 50
第五章 結論 54
參考文獻 55

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