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研究生:吳柏翰
研究生(外文):Wu,Bo-Han
論文名稱:晶圓取放機械手臂之健康狀態預診
論文名稱(外文):Prognostics and Health Management of a Wafer Handling Robot Arm
指導教授:鍾官榮
指導教授(外文):Chung, Kuan-Jung
口試委員:鍾官榮王可文盧銘詮
口試委員(外文):Chung, Kuan-JungWang, Ker-WinLu, Ming-Chyuan
口試日期:2017-6-27
學位類別:碩士
校院名稱:國立彰化師範大學
系所名稱:機電工程學系所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:中文
論文頁數:72
中文關鍵詞:機械手臂預後和健康管理自動光學檢測維納過程蒙地卡羅模擬方法
外文關鍵詞:Robot ArmPrognosis and Health ManagementAutomated Optical InspectionWiener ProcessMonte Carlo simulation
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近年來機械手臂得到應用的比例取得顯著的增長。對於想有效提高生產率和降低成本的公司來說,是個重要的投資。因此,當設備發生意外的停機,對於製造商而言就意味著損失。為了將這種痛苦降到最低,製造商們正在尋求或開發新的健康檢測、診斷、預後和維護的解決方法。上述所提之解決辦法,被統稱為預後和健康管理(Prognostics and Health Management, PHM)技術。隨著科技日新月異的發展,應用的領域與環境也就更加複雜。因此,機械手臂系統的可靠度也就更加關鍵。希望藉由開發的預後模型,能夠協助業界能夠快速評估其機械手臂系統的健康狀態。
本研究針對半導體生產設備之機械手臂發生的送料故障。以自動光學檢測技術為概念,運用CCD攝影機搭配晶圓取放機械手臂構成一基本型視覺辨識系統。再建立一數據驅動的預後模型,它包含一個隨機的維納過程,並從觀察具體的位置數據,實行基於粒子濾波器的蒙地卡羅模擬方法。最後將基本型視覺辨識系統與數據驅動的預後模型構成一機械手臂健康狀態預診系統,達到健康狀態診斷與預估殘餘使用壽命的效果。
In recent years, the applications of robot arm significantly have been increased due to the industry 4.0 requirement. It is an important equipment of automatic manufacture line for productivity increase and costs reduce. Therefore, when the robot is unexpectedly shutdown, it may loss of yield and increase downtime of the equipment in the process station. With the rapid development of Industry 4.0, the application of the fabrication field and the environment will be more complex, the in-line lifetime prediction of the robot arm system is even more critical and hard to deliver the precise results. In order to minimize this issue, manufacturers are developing new technology for in-line robot diagnosis, prediction failure time and then optimize the yield and equipment maintenance named Prognosis and Health Management, PHM.
This study is focus on the developing the system which enabling sends the failure information when the robot arm in operation at a semiconductor manufacturing process. The automated optical inspection (AOI) technology is applied to assembly a basic/advaced visual identification system including one high-level CCD camera and image-handling software. Thee data-driven prognostic model, it contains a Wiener stochastic process and a Monte Carlo simulation based on the particle filter, was applied to predict the lifetimes of the robot arm. Furthermore, the residual useful lifetimes were updated continually to predict the maintenance time of the robot arm during the operation period.
摘要 I
ABSTRACT II
謝誌 III
目錄 IV
表目錄 VII
圖目錄 VIII
第一章 緒論 1
1-1 研究背景 1
1-2 研究動機與目的 3
1-3 論文架構 6
第二章 理論與文獻回顧 7
2-1機械手臂簡介 7
2-1-1 機械手臂零件介紹 8
2-1-2 機械手臂指令 10
2-2 機械手臂定位誤差 12
2-2-1 誤差檢測方式及定義 13
2-3 自動光學檢測 14
2-4 可靠度原理 17
2-4-1 可靠度定義 17
2-4-2 浴缸失效模式 19
2-4-3 機率密度函數與可靠度函數 20
2-4-4 平均失效時間 22
2-5 隨機過程老化模型 24
2-5-1 維納過程老化模型 25
2-5-2 伽瑪過程老化模型 26
2-6 蒙地卡羅理論 28
2-6-1蒙地卡羅模擬歷史 28
2-6-2蒙地卡羅模擬方法 29
2-6-3不確定性分析 30
2-7 粒子濾波器 31
2-7-1 初始化 32
2-7-2 更新權值 32
2-7-3 重新取樣 35
第三章 機械手臂健康狀態預診系統 39
3-1健康狀態預診系統-硬體建置 40
3-1-1 CCD攝影機模組 41
3-1-2 感測器與觸發訊號 43
3-2 健康狀態預診系統-軟體設計 46
3-3 健康狀態預診系統-預測 52
3-4 健康狀態預診系統-工作流程 55
第四章 預診結果與討論 58
4-1 機械手臂健康狀態預診系統 58
4-2 健康狀態預診 62
4-3 健康狀態追蹤 63
第五章 結論與未來展望 67
5-1 結論 67
5-2 未來展望 68
參考文獻 69
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