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研究生:葉俊吾
研究生(外文):Junwu Yeh
論文名稱:運用類神經網路建構SMT錫膏印刷製程品質管制系統
論文名稱(外文):Using Artificial Neural Networks to Build a Quality Control System of SMT Stencil Printing Process
指導教授:楊大和楊大和引用關係
指導教授(外文):Taho Yang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:製造工程研究所碩博士班
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:2002
畢業學年度:90
語文別:中文
論文頁數:105
中文關鍵詞:錫膏印刷類神經網路表面黏著技術實驗設計
外文關鍵詞:Neural NetworksDesign of ExperimentStencil PrintingSMT
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為因應現今消費性電子產品的輕、薄、短、小趨勢,電子組裝技術亦隨著不斷改進。表面黏著組裝技術 (Surface Mount Technology, SMT) 已加速地取代傳統波焊製程 (Wave Soldering) ,儼然成為現代電子組裝產業主流,其製程優點在於降低生產成本並製造出高品質之電子產品。但於極端複雜的表面黏著組裝技術生產環境裡,製程中充滿著許多不確定的生產變數組合,尤其是在高密度之電子元件組裝,更成為基板組裝業之重大課題。若無法妥善控管製程參數及原物料特性,極可能產生不良焊性 (Solderability) ,導致產品品質下降、增加額外之生產成本;根據文獻及業界資深工程師的經驗得知,52%~71%之焊性不良源起於錫膏印刷製程 (Solder Paste Printing Process)。
本研究針對錫膏印刷製程單元,利用8個重要參數(錫膏黏度、鋼板厚度、零件腳距、鋼板與銲墊比例、間隙、印刷速度、刮刀壓力、錫膏顆粒)以分析錫膏印刷製程。並運用實驗設計及類神經網路以建構一套製程品質管制之衡量標準,並可配合統計製程品管(SPC)軟體以提昇焊性及產品品質。
Surface Mount Technology (SMT) assembly is the placement and attachment of electronic components to the surface of a printed circuit board, and it has become the key technology to transform manufacturing in the electronics industry to continuously respond to the needs of the global market. The relationship between the input/output variables in SMT acts nonlinearly and severely. The stencil printing is one of the most critical stages and accounts for 52~71% of overall soldering defects.
This research will help in understanding the solder paste stencil printing process and identifying the critical variables that influence the volume of deposited solder paste. Through design of experiment and neural networks, a quality control system of stencil printing is established that helps engineers in troubleshooting the malfunctioned process and to improve solderability.
中文摘要 i
英文摘要 ii
誌 謝 iii
目 錄 iv
圖目錄 vii
表目錄 ix
第一章 緒論 1
1.1 簡介 1
1.2 研究背景與動機 1
1.3 印刷電路板製程簡介 2
1.3.1 表面黏著組裝技術 3
1.3.2 表面黏著生產線機器配置 3
1.3.3 表面黏著製程 4
1.3.4 波焊製程 8
1.3.5 自動測試 8
1.4 研究目的 9
1.5 研究方法與流程 9
1.6 論文架構 11
第二章 文獻回顧 12
2.1 表面黏著技術 12
2.1.1電子組裝之品質控管—表面黏著組裝技術 12
2.1.2電子組裝之品質控管—錫膏印刷製程 12
2.2 類神經網路 25
2.2.1類神經網路基本概念 25
2.2.2類神經網路在生產規劃與管制上之應用 27
2.3 實驗設計 30
2.3.1 基本原理 30
2.3.2 實驗設計之目的 31
2.3.3 部分因子設計 32
第三章 模式理論與系統架構 34
3.1 錫膏印刷製程系統架構 34
3.1.1 問題描述 34
3.1.2印刷垂直與平行方向 35
3.2 類神經網路 36
3.2.1類神經網路模式 38
3.2.2 倒傳遞類神經網路 42
3.2.2.1 倒傳遞類神經網路架構 43
3.2.2.2 倒傳遞類神經網路學習演繹法 44
3.3類神經網路模式之建構 48
3.3.1 網路模式選擇 48
3.3.2 網路參數設定 49
3.3.3決定終止條件 50
3.3.4 進行訓練 51
3.3.5 網路績效評估與製程能力指標之導入 51
第四章 實例說明 53
4.1實例環境資料 53
4.2 錫膏印刷品質管制系統模式之構建 54
4.2.1 運用實驗設計建立與蒐集結構化製程資料 55
4.2.2 建立類神經網路的預測模式 57
4.2.3 預測與分析 62
4.2.4 網路績效評估指標 62
4.3 執行結果 63
4.3.1 水平方向 63
4.3.1.1 選取網路架構 63
4.3.1.2交叉驗證法 64
4.3.2 垂直方向 65
4.3.2.1 選取網路架構 65
4.3.2.2交叉驗證法 66
4.4資料分析與比較 67
4.5倒傳遞網路與通用迴歸網路之比較 73
4.5.1 通用迴歸型類神經網路 73
4.5.2 網路績效比較 74
4.6製程能力指標 、 75
第五章 結論 79
5.1 結論 79
5.2 未來研究方向 80
參考文獻 82
附錄A:243筆實驗因子水準組合程式碼 88
附錄B:243筆實驗因子水準直交表組合 90
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