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研究生(外文):Chien-Chung Chou
論文名稱(外文):Research on Application of Data Mining to Anomalistic Operation of Robot Spraying Equipment-An Example of "K" factory
中文關鍵詞:資料探勘關聯規則Robot 噴塗設備
外文關鍵詞:Data miningAssociation rulesRobot spraying equipment
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本研究旨在運用資料探勘(Data Mining)技術,探勘過往監測及檢修數據,期找出 Robot 噴塗設備異常之關聯規則,適時提出因應措施,以達到降低備品庫存及相關人力成本及之目的,並確保相關設備可正常運作,避免因設備異常造成的損失,協助企業追求利潤、提高企業競爭力。

The automobile industry in Taiwan has made significant progress in the past fifty years; especially after the implementation of Taiwan Automobile Development Policy, the techniques have been greatly improved with high quality, which has made Taiwan a crucial component supply center in Asia.
However, due to the heavy dependence on key technologies from the parent factory, most car manufacturers in Taiwan still remain in the field of modifying and trimming. Besides, they need the related companies in Japan to offer assistance of maintenance and component supplying. Because of that, plus time-consuming purchase, technique aid from specialists in Japan, and tracking of long-term inventory, factories here in Taiwan usually have to spend a considerable fortune every year to pay for human resources.
This research is to figure out how we can make more profits for businesses, cost down on human expenses and enforce business competitiveness through Data mining, detecting precedent data base and surveillance, finding out association rules of anomalistic operation of Robot spraying equipment, and proposing an appropriate solution in time.

書 名 頁 i
論文口試委員審定書 ii
授 權 書 iii
中文摘要 iv
英文摘要 v
誌  謝 vi
目  錄 vii
表 目 錄 ix
圖 目 錄 x
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 1
第三節 論文架構與研究流程 1
第二章 文獻探討 3
第一節 汽車塗裝製程 3
第二節 資料探勘 4
壹 資料探勘定義 6
貳 資料探勘概述 7
參 資料探勘的應用 11
第三節 關聯法則 12
壹 關聯法則相關定義 12
貳 Apriori演算法 13
參 關聯法則的相關應用 17
第三章 研究方法 19
第一節 問題描述 19
第二節 資料描述 19
第三節 汽車塗裝製程 21
第四節 研究方法流程圖 26
第五節 Frequent-pattern growth (FP-growth)演算法 27
第四章 研究結果 38
第一節 資料內容 38
第二節 程式開發 38
第三節 實例介紹 40
第五章 結論與未來研究方向 44
第一節 結論 44
第二節 未來研究方向 44
第三節 研究限制 44
參考文獻 45

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