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    Please use this identifier to cite or link to this item: http://ccur.lib.ccu.edu.tw/handle/A095B0000Q/122


    Title: 基於等效輪廓誤差之雙軸線上學習控制與學習控制之轉換時機探討
    Authors: 陳子安;CHEN, TZU-AN
    Contributors: 機械工程系研究所
    Keywords: 線上學習控制;等效輪廓誤差;最佳轉換時機;online learning control;equivalent contour error;best switch timing
    Date: 2018
    Issue Date: 2019-05-23 12:52:37 (UTC+8)
    Publisher: 機械工程系研究所
    Abstract: 智能化已是近年來工具機技術發展的主要方向之一,不僅是傳統一味的大量生產,現在更注重的是精度優化及客製化。透過即時監控,大數據分析及工廠整合設計,使生產過程更為有效,使生產結果滿足各種不同需求。線上學習控制是在離線學習控制的基礎上,去發展更為節省時間的演算法。此學習控制的原理是利用前一次加工的誤差,根據與路徑相關之演算法,來修正下一次的命令,可使機台達到更高的精度;且本演算法是在不改變回授控制的前提下,於閉迴路系統外加入學習控制的功能,因此能應用在各個機台上,對機台做優化。 傳統學習控制是以追蹤誤差為目標,然而實際上能真正影響工件精度的是輪廓誤差。目前只有少數以輪廓誤差為目標的學習控制,原因在於輪廓誤差的計算並不易。本論文所提之等效輪廓誤差是以路徑方程式來建立,不僅較易記算,也可以相當程度來等效於實際輪廓誤差。 本研究所提之線上學習控制,在應用上可用於各種機台的優化,例如多軸工具機甚至機器人等。我們實際應用到了五軸工具機上,透過實驗結果可以證實,線上學習控制不僅可達到離線學習控制相去不遠的結果,在計算時間上更能縮短不少。 本研究也提出另一個優化學習控制的方向,也就是轉換時機的決定。我們透過實驗發現的最佳轉換時機會根據不同情況及條件有所變化,如果能找出其關係式,就能使學習控制在各種路徑達到最佳的效果。
    Intelligence is one of the main developing direction in recent years. Not only to produce massive product, it’s more important to optimize the accuracy and customize. By immediately monitoring、big data analysis and design of the factory, we can make the producing process more efficient and the result to satisfy different kind of demand. Online learning control is based on offline learning control. The purpose is to develop an algorithm that can save more time. The principle of this learning control is to use the error of the previous processing, according to the algorithm related to the path, to correct the next command and the machine can achieve higher precision. The algorithm does not change the feedback control. Under the premise, the function of learning control is added outside the closed loop system, so it can be applied to each machine to optimize the machine. Traditional learning control is aimed at tracking error, but in reality it is the contour error that really affects the accuracy of the workpiece. At present, there are only a few learning controls that aim at contour errors, because the calculation of contour errors is not easy. The equivalent contour error proposed in this paper is established by the path equation, which is not only easy to calculate, but also equivalent to the actual contour error. The online learning control proposed by this research can be applied to the optimization of various machines, such as multi-axis machine tools and even robots. We actually apply it to the five-axis machine tool. Through the experimental results, we can confirm that the online learning control can not only achieve the results really close to that of offline learning control, but also shorten the calculation time. This study also proposes another direction to optimize learning control, which is the decision to switch timing. The best switch timing we find through experiments vary according to different situations and conditions. If we can find out the relationship, we can make learning control achieve the best results in various paths.
    Appears in Collections:[機械工程學系] 學位論文

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