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dc.contributor.authorChang, Kai-Shengen_US
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dc.description.abstract隨著科技工業的發展,東西越來越細緻與精小,當我們要針對加工元件進行量測時,工業界上普遍使用三次元量床(coordinate measuring machines,CMM)來進行量測。本研究針對三次元量床進行量測路徑的規劃,接觸式量床量測精度高,也方便撰寫量測程式,更可以修改量測路徑做彈性的應用。我們利用遺傳演算法求解旅行者問題最短拜訪路徑的概念,撰寫一個遺傳演算法的程式,藉由輸入距離矩陣的設計,我們可以將原本的單純量測點群排序問題擴張為考慮多種探頭的情況下,進行的最短路徑規畫。 本研究分為兩部份,第一部份首先針對旅行者問題撰寫遺傳演算法,利用貪婪演算法的觀念修改其交配運算子,以TSPLIB[註]的範例作為演算法運行的檢視。第二部份將待測物依照幾何形狀規劃量測點,接著以自定的避障原則量出量測點間的距離,然後放入演算法運行,求出最佳路徑排序及路徑總長,最後分析結果及討論。zh_TW
dc.description.abstractThis research focuses on CMM with planning of route, the CMM with contact-type probe have high precision, and it is convenient for person to write procedure. It also revise examine route that make elastic application even usefully. We solve the Traveling Salesman Problem with a general idea of Genetic Algorithm, and perform of Genetic algorithm. We can extend examine original simple distance matrix for considering multi-probe with the design of new matrix, and show the rule of shorting path. With the development of scientific and technological industry, the parts are more careful and attentive. We usually use coordinate measuring machines to examine to the process parts on industry. This research is divided into two parts, one part is write a form to solve Traveling Salesman Problem by Genetic algorithm, and we use the idea of greedy algorithm to modify the crossover operator. We check the result of algorithm by the case of TSPLIB. The Second part, we plan the measure points with the rule of geometry, and we count the distance between measure points. Then we put the result into the algorithm to find the best route of measuring and path value. Finally we analysis the result and discuss.en_US
dc.description.tableofcontents摘要 i Abstract ii 致謝 iii 目 錄 iv 表目錄 vi 圖目錄 vii 第一章 緒論 1 第一節 研究動機與研究目的 1 第二節 相關文獻回顧 2 第三節 研究方法與步驟 4 第四節 論文架構 4 第二章 CMM三次元量床及量測路徑規畫 7 第一節 三次元量床之構造 7 第二節 非接觸式量測 10 第三節 接觸式量測 10 第四節 量測點分配與路徑規畫 10 第三章 旅行商問題 14 第一節 旅行商問題與量測路徑 14 第二節 處理旅行商問題演算法之介紹 15 3.2.1傳統啟發式解法(Heuristics) 15 3.2.2多用途啟發式方法(Meta-heuristics) 16 第四章 貪婪演算法 18 第一節 演算法介紹 18 第二節 演算法於最小路徑的適用 19 第五章 基因演算法 20 第一節 基本觀念與名詞 20 第二節 基因演算法的規劃 21 5.2.1個體的編碼方式 21 5.2.2適應度函數(fitness) 21 5.2.3挑選(select)機制 23 5.2.4交配(crossover)機制 25 5.2.5突變(mutation)機制 31 第三節 多探針TSP問題基因演算法運算子規劃 32 5.3.1基因編碼方式及適應度函數設計 32 5.3.2選擇運算子 32 5.3.3交配運算子 35 5.3.4突變運算子 39 5.3.5多探頭矩陣設計 39 5.3.6一般GA的虛擬碼 40 第一節 實驗參數及步驟 42 第二節 待測工件分析及最佳路徑 54 第七章 結論與未來展望 64 第一節 結論 64 第二節 未來展望 64 參考文獻 66zh_TW
dc.subjectCoordinate Measuring Machinesen_US
dc.subjectPath Planningen_US
dc.subjectTraveling Salesman Problemen_US
dc.subjectGenetic Algorithmen_US
dc.titleThe Path Planning for multi-probe Coordinate Measuring Machine on the Genetic Algorithmen_US
dc.typeThesis and Dissertationzh_TW
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