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研究生: 李昭彣
Lee, Chao-Wen
論文名稱: 以徑向基底函數類神經網路於開放式虛擬工具機系統進行切削力預測之研究
Studies on Cutting Force Prediction with Radial Basis Function Neural Networks in Open-Architecture Virtual Machine Tool System
指導教授: 李榮顯
Lee, Rong-Shean
學位類別: 碩士
Master
系所名稱: 工學院 - 機械工程學系
Department of Mechanical Engineering
論文出版年: 2018
畢業學年度: 106
語文別: 中文
論文頁數: 104
中文關鍵詞: 徑向基函數類神經網路切削力預測虛擬工具機通訊連線
外文關鍵詞: RBFNN, cutting force prediction, virtual machine tool, artificial neural networks
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  • 五軸工具機為高精度加工帶來了便利,然而隨著加工外型日趨複雜,加工過程中所產生的誤差和危險,更加不可預期。為確保加工過程順利,往往必須反覆測試程式並採取相對保守的加工條件,在時間成本上花費甚鉅。市面上常見的商用加工驗證軟體如NX及Vericut,需耗費額外成本製作不同控制器廠牌或型號的CNC控制器模擬器來進行驗證。
    本文發展出將類神經網路結合開放式虛擬工具機的系統,此系統具有資料分析的能力。透過網路通訊建立商用控制器與虛擬工具機間的通訊協定,藉由路徑插補解譯模擬真實加工情況,包含運動模擬、切削模擬、碰撞偵測等。模擬所獲得的切削幾何資訊與從控制器取得的加工資訊匯入類神經網路所建立的模型得以預測結果,此預測結果能提供使用者進行分析。本文同時設計友善之人機介面,方便操作者建立預測模型,調整模型參數與前處理資料,並以圖表化方式呈現結果。虛擬加工環境則以MVC(Model-View-Controller)架構設計,建立仿照真實加工環境之系統。
    最後本文透過與不同商用控制器通訊,以鞋模三軸加工案例與葉輪五軸加工案例呈現模擬結果,並比較類神經網路預測模型所得預測切削力與物理力學模型計算切削力的結果與趨勢。

    The five-axis machine tool brings performance to high-precision manufacturing. However, with complex workpieces unpredictable errors and dangers during the manufacturing process increase. In order to ensure the success of the manufacturing process, it’s necessary to repeat tests and adopt more conservative processing strategies, which leads to a great expense on machining time.
    This research develops a system that combines artificial neural networks and an open virtual machine tool, which could analyze process data. By establishing the communication protocol between the commercial controller and the virtual machine tool via network communication, which can receive path interpolation from controller and simulate machining process, including motion simulation of the machine tool, cutting simulation, and collision detection. The geometry data by simulating and manufacturing process information from controller are taken as the inputs import into the neural network. It can construct the predictive model. Besides, human-machine interface has been designed in this thesis, which is convenient for the end-user to set up models, adjust parameters, pre-process data, and presents the results. The virtual machining environment is designed base on MVC architecture to create realistic circumstances.
    Finally, this research creates the communication with different commercial controllers, the simulation results are presented in the case of shoe mold in 3-axis machining tool and the case of the wheel rim in 5-axis machining. The research also shows graphic results which the cutting force between the neural network prediction model and calculated physical model can be compared.

    摘要 I ABSTRACT II 誌謝 VII 總目錄 VIII 表目錄 XII 圖目錄 XIII 符號說明 XVII 專有名詞縮寫 XIX 第一章 緒論 1 1.1研究動機 1 1.2文獻回顧 3 1.2.1開放式虛擬工具機系統 3 1.2.2切削力模型預測 5 1.3研究目的與方法 9 1.3.1研究目的 9 1.3.2研究方法 10 第二章 基本理論 12 2.1開放式架構虛擬工具機系統 12 2.1.1機台運動模組 12 2.1.2材料移除模組 16 2.1.3碰撞偵測模組 17 2.1.4網路通訊模組 19 2.2切削力預測模型 23 2.2.1三軸計算铣削力模型 23 2.3 類神經網路 25 2.3.1徑向基函數神經網路介紹(RBFNN) 26 第三章 開放式虛擬工具機系統 29 3.1通訊系統架構 29 3.1.1通訊介面架構與操作流程 30 3.1.2新代控制器 32 3.1.3台達控制器 35 3.1.4控制器通訊人機介面 38 3.2虛擬工具機系統架構 41 3.2.1虛擬工具機加工設置 41 3.2.2虛擬工具機主介面 42 3.3人機介面與MVC模組 45 3.3.1開放式虛擬工具機模型模組(Model) 46 3.3.2開放式虛擬工具機畫面模組(View) 50 3.3.3開放式虛擬工具機控制模組(Controller) 52 第四章切削力預測模型 56 4.1切削力預測模型準備 58 4.1.1類神經網路 58 4.1.2資料準備 59 4.1.3資料前處理 64 4.2切削力預測模型創建 67 4.2.1訓練過程 67 4.2.2初步模型 71 4.2.3確立模型 73 4.3切削力預測模型應用 78 4.3.1 RBFNN人機介面 78 4.3.2結合開放式虛擬工具機系統 79 4.3.3進給率優化 80 第五章 結果與討論 82 5.1虛擬工具機之加工模擬與碰撞檢測驗證 82 5.1.1台達控制器 82 5.1.2新代控制器 86 5.2虛擬工具機之切削力預測 93 第六章 結論與建議 99 6.1結論 99 6.2建議 101 參考文獻 102

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