臺灣博碩士論文加值系統

近幾年隨著金屬加工技術的成熟發展，加工業者對於加工中心機長時間運轉的需求也逐漸提升，以往工具機的主軸在長時間運轉下 會出現主軸熱變位之現象，此種狀況會造成工具機精度的誤差，因此本研究針對工具機主軸熱變位提出主軸熱溫伸補償系統，此系統是採用NC-PC的方式針對FANUC 0I-MF控制器進行熱變位預測系統的開發，並撰寫PMC程式進行熱變位補償。本研究主要分為三個部分，第一部分，針對主軸熱變位量測系統進行開發，主要使用K-Type熱電耦溫度感測器進行主軸溫度量測與紀錄，並結合電容式位移計量測主軸Z方向的熱神長量，其中熱電耦感測器必須透過溫度模組量測其訊號，而電容式位移計須採用NI-DAQ擷取電壓訊號並轉換為位移量，因此本研究針對溫度量測模組與DAQ開發一套量測系統，並且能夠紀錄量測之溫度與位移量，做為主軸熱變位量測所需使用；第二部分，開發熱溫升模型建置與補償系統，這部分主要針對第一部分的熱變位量測系統所紀錄之數據，進行熱變位模型與智能化補償系統進行建置，其中於熱變位模型之建置採用類神經演算法訓練其預測模型，並將模型所預測之結果實際補償至機台驗證其補償精度，因此本研究於此部分開發了一套智能化熱變位補償系統，使工具機設備能夠自動修正主軸之熱變位量；第三部分，針對溫升補償系統開發雲端物聯網平台，本研究使用MQTT通訊協定與Visual Studio進行雲端網頁之建置，藉由Asp.Net MVC架構開發一套主軸熱變位預測網頁看板，並藉由MQTT通訊協定將主軸的熱變位、溫度等資訊透過熱變位補償系統傳遞至雲端伺服器，使相關資訊能夠透過網頁監視主軸熱變位。本研究針對主軸熱變形實際量測結果發現，Z 方向伸長量的增加與溫度的上升成正比，伸長量達56μm，然而針對主軸軸承端與鑄件端溫度進行倒傳遞類神經建模，應用在三軸加工機上進行實際運轉8小時的進行長時間的智能化補償，由補償結果顯示Z方向熱位移能夠縮小至10μm。

In recent years, with the mature development of metal processing technology, processing industry has gradually increased the demand for long-term operation of machining center machines. In the past, the main shaft of machine tool would have the phenomenon of thermal displacement of the main shaft under long-term operation. This situation will cause Because of the error of machine tool accuracy, this research proposes a spindle thermal extension compensation system for the thermal displacement of the machine tool spindle. This system uses the NC-PC method to develop a thermal displacement prediction system for the FANUC 0I-MF controller. Write PMC program for thermal displacement compensation. This research is mainly divided into three parts. The first part is to develop the spindle thermal displacement measurement system, mainly using K-Type thermocouple temperature sensor for spindle temperature measurement and recording, combined with capacitive displacement measurement The thermal length of the Z direction of the spindle. The thermocouple sensor must measure its signal through a temperature module, and the capacitive displacement meter must use NI-DAQ to capture the voltage signal and convert it into a displacement. Therefore, this research focuses on temperature The measurement module and DAQ develop a measurement system, and can record the measured temperature and displacement, as required for the measurement of the thermal displacement of the spindle; the second part is to develop the thermal temperature rise model building and compensation system , This part mainly focuses on the data recorded by the thermal displacement measurement system in the first part, and builds the thermal displacement model and the intelligent compensation system. Among them, the thermal displacement model is built using neural algorithms to train its prediction Model, and actually compensate the results predicted by the model to the machine to verify its compensation accuracy. Therefore, in this part of the research, an intelligent thermal displacement compensation system was developed to enable the machine tool to automatically correct the thermal displacement of the spindle ; The third part is the development of a cloud IoT platform for the temperature rise compensation system. This research uses the MQTT communication protocol and Visual Studio to build cloud web pages, and develops a set of spindle thermal displacement prediction web signboards using the Asp.Net MVC architecture. And through the MQTT communication protocol, the spindle's thermal displacement, temperature and other information are transmitted to the cloud server through the thermal displacement compensation system, so that the relevant information can be monitored through the webpage. According to the actual measurement results of the thermal deformation of the spindle, this study found that the increase in the Z-direction elongation is proportional to the temperature rise, and the elongation reaches 56μm. However, the inverse transmission nerve modeling for the temperature of the spindle bearing end and the casting end is applied in three The shaft processing machine performs long-term intelligent compensation for 8 hours of actual operation. The compensation result shows that the thermal displacement in the Z direction can be reduced to 10μm.

摘 要 i
ABSTRACT iii
目 錄 v
表 目 錄 vii
圖 目 錄 viii
符 號 說 明 ix
第一章、 緒論 1
1.1 研究背景與動機 2
1.2 研究目的 2
1.3 論文架構 2
第二章、 文獻回顧 4
2.1 主軸熱變位相關研究 4
第三章、 研究架構與方法 11
3.1 研究架構與流程 11
3.2 硬體及系統架構 12
3.2.1 主軸位移量測設備 12
3.2.2 溫度量測硬體設備 14
3.2.3 實驗機台介紹 15
3.3 主軸溫度與熱變位量測系統 15
3.3.1 主軸熱變位量測系統 20
3.4 熱溫升模型建置與補償系統開發 21
3.4.1 主軸熱變位預測模型建置 21
3.4.2 主軸熱變位補償系統開發 28
3.5 物聯網與雲端溫升預測平台建置 33
3.5.1 雲端資料管理系統建置 33
3.5.2 MQTT通訊協定 34
3.5.3 雲端資料庫建置 37
3.5.4 雲端主軸溫升監視網頁開發 39
第四章、 研究結果 41
4.1 熱變形量測實驗 41
4.1.1 熱變形量測實驗 41
4.1.2 熱變形量測軟硬體建置 41
4.1.3 電容式位移計精度校正補償 43
4.1.4 實際主軸熱變位量測與模型建置結果 44
4.2 主軸熱變位實驗建模與補償結果 47
4.2.1 主軸熱變位補償系統建置 47
4.3 物聯網與雲端溫升量測系統 48
4.3.1 通訊協定應用 48
4.3.2 PostgreSQL資料庫建立 49
4.3.3 雲端主軸熱變位監視網頁 50
第五章、 結論與未來展望 52

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