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    Please use this identifier to cite or link to this item: http://ccur.lib.ccu.edu.tw/handle/A095B0000Q/144


    Title: 使用預測方法達成即時性五軸工具機碰撞偵測之研究;A Research of Achieving Five-axis CNC Machine Tool Real Time Collision Detection with Predictive Methods
    Authors: 賴泓逸;LAI, HUNG-YI
    Contributors: 機械工程系研究所
    Keywords: 碰撞偵測;預測;灰色理論;實體幾何模型;Collision detection;Forecast;Grey Theory;constructive solid geometry
    Date: 2018
    Issue Date: 2019-05-23 12:52:42 (UTC+8)
    Publisher: 機械工程系研究所
    Abstract: 五軸CNC工具機造價高昂,因此防止撞機的技術就相當重要。在工具機防碰撞的領域中,大多數的應用方式都是藉由預讀加工的NC code(G-code)得到加工的路徑,再以各式各樣的碰撞偵測演算法計算刀具與各軸機具間的狀態。然而這樣的方式並不是真正的即時性防碰撞,反而比較接近干涉的模擬,並且無法應用於沒有加工碼的情況中。為解決上述問題,本文引入統計學中的預測模型,針對工具機的歷史軌跡進行預測,獲得下一個時間點機台可能的座標位置,提早進行碰撞偵測來迴避碰撞的發生。 本文比較了數種預測模型並加以改進,修改後的SAIGM預測模型針對Bezier曲線能達到平均誤差0.006微米,最大誤差0.521微米的預測結果。利用本文改良後的SAIGM預測模型,可以使用儲存的工具機軌跡資料來預測6個取樣時間後的機台位置。 在模型建構方面,本文使用(Constructive Solid Geometry)實體構造幾何模型來建構機台模型,而非三角網格,來降低測試的物件數量,進而使分離軸定理的碰撞偵測時間低於0.1ms,平均偵測時間約為44μs。最後以OpenGL呈現電腦動畫做驗證。 本文目的在於發展一套應用於五軸CNC工具機之即時性防碰撞偵測技術,並且使其能應用於實際的五軸工具機之中。本文提出融合預測方法與碰撞偵測的程式架構在模擬中皆能在1ms內完成,可應用於實際的五軸工具機中。文末針對進給率較大的路徑進行分析,在物件數量極低的情況下也能在1ms內完成整個程式的計算。
    Five-axis CNC machine tools are expensive but necessary to today’s industry. To avoid the machine tools collision or accident happening, collision detection technology has been much more important than before. Those machine tools collision detection structures were often done by pre-reading the command or interpolation points from the user. Once they got those pre-reading contours, they will be applied to a variety of collision detection methods. However, those structures are not real-time, more like simulation of interference. On the other hand, they can’t apply to the situation without G-codes. To achieve real-time five-axis CNC machine tools collision detection, we will develop a new structure of machine tools collision detection by utilizing encoders’ data and forecast model. By forecasting the position of machine tools, we will able to avoid collision happening when the SAT(Separating-Axis-Theorem) finish. In this research, we compare several kinds of prediction models and improve the SAIGM(self-adaptive intelligence grey predictive model) forecast model. Forecasting Bezier curve contour with improved SAIGM, the average error is 0.006μm and the maximum error is 0.521μm. The improved SAIGM forecast model can be used to forecast up to 6 sampling times. CSG(Constructive Solid Geometry) method is utilized to represent the five-axis CNC machine tools. The computation time of SAT with limited models is lower than 0.1ms, the average computation time is 44μs. Simulation results will show with OpenGL in the end of program. The computation time of this real-time five-axis CNC machine tools collision detection algorithm are totally below 1ms, which means it could be possible installed in real industry. Even with a higher feedrate, as long as the CSG model’s number is small, the computation time will still below 1ms.
    Appears in Collections:[機械工程學系] 學位論文

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