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研究生:彭晟輔
研究生(外文):Cheng-Fu Peng
論文名稱:環保點膠製程研發與創新參數設計
論文名稱(外文):Development of Environmental Conscious Adhesive Dispensing Process and Innovative Parametric Design
指導教授:黃乾怡黃乾怡引用關係
指導教授(外文):Chien-Yi Huang
口試委員:劉大昌應國卿
口試委員(外文):Ta-Chung LiuKuo-Ching Ying
口試日期:2012-06-20
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:工業工程與管理系碩士班
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:58
中文關鍵詞:表面黏著技術錫鉍合金田口方法主成份分析灰關聯分析類神經網路
外文關鍵詞:surface mount technology58Bi/42SnTaguchi methodsprincipal component analysisgrey relational analysisartificial neural network
相關次數:
  • 被引用被引用:13
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近年來全球電子工業不斷創新呈現快速發展趨勢,運用製程優化提昇產品良率是業界研發的重點方向。為因應電子產品微小化、多功能等市場需求,電子構裝製程多以雙面板或混合表面黏著技術及波峰焊製程。波峰焊製程於穿孔式元件組裝時,通常藉由波焊載具屏蔽第一面PCB上之SMD元件以避免元件偏移或沖刷掉落的情形,然而使用載具將增加成本。本研究以音響PCBA產品為例,針對低成本之CEM-1板材及0805陶瓷電容,開發環保點膠製程,並使用低熔點之無鉛環保焊料(錫鉍合金58Bi/42Sn),且點膠之烘烤與迴焊將同步進行,以提升生產效率。由於生產過程中PCBA搬運可能造成之應力,及波焊製程中熔錫擾動造成多方向性沖刷PCB上之元件,故於室溫及高溫環境下分別考量電容元件直向及縱向之膠水黏著強度(即室溫及高溫直推、橫推力),所以屬於多品質特性問題。運用田口方法設計實驗,考慮點膠位置、壓著時間、熱固溫度及迴焊輸送帶速度等控制因子。應用主成份分析與主成份灰關聯分析等統計方法,並提出創新之類神經智慧型參數設計,分別決定最適製程參數組合解決多重品質特性問題。再依各分析方法之最適參數組合製作試驗樣本並進行推力實驗。結果顯示,本研究開發之類神經網路方法所建議之最適參數組合優於其他兩種方法。並將其導入波焊製程產線,觀測元件沖刷掉落現象以評估良率之改善成效。最後,針對傳統製程與環保點膠製程兩種生產模式進行成本評估。結果顯示,本研究開發之環保點膠製程與創新參數設計可有效降低生產成本約65%,同時兼顧環保效益。

In recent years, the microelectronic manufacturing grows rapidly. Process optimization to production yield is major focus of the industry. To meet the requirement in product miniaturization and multi-function, surface mount technology (SMT) and through-hole technology (THT) are used to assembly the double sided printed circuit board (PCB). In through-hole technology, the soldering carrier is commonly used to cover the component assembled on the first side. This is to avoid the component from being contact with the molten solder and fall off. However, using the carrier will increase the production costs. This study aims at a sound card and investigates the adhesive dispensing process to replace the necessity of carrier. The CEM-1 PCB material and the 0805 passive components are under consideration. The lead-free solder 58Bi/42Sn with low melting point is used in this research.
This study consider the shear strength of components from both the horizontal and vertical directions at normal and high temperature. This is to simulate the effects of molten solder acting on the passive components. Thus, this study involves multi-quality characteristics. We used Taguchi method to investigate the effect of dispensing position, pressing time, curing temperature and the conveyor speed. The principal component analysis (PCA), principal component gray relational analysis and innovative artificial neural network are exercised to handle the multiple quality characteristics and determine the optimal process parameters. The result showed that the parameter determined by innovative artificial neural network parameters design is better than the others. The optimal process is implemented in the production line to verify its effectiveness.


目錄
摘要 i
ABSTRACT iii
誌謝 v
表目錄 viii
圖目錄 ix
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 4
1.4 研究範圍與限制 5
1.5 研究流程 5
第二章 文獻探討 7
2.1 SMT製程 7
2.2 膠水環氧樹酯 10
2.3 田口方法 12
2.4 多重品質特性 14
2.4.1 主成份分析 14
2.4.2 主成份灰關聯分析 14
2.4.3 類神經網路 15
第三章 研究方法 18
3.1 田口式品質工程簡介 18
3.1.1 直交表 18
3.1.2 信號雜音比 19
3.1.3 參數設計 20
3.1.4 確認實驗 21
3.2 主成份分析 23
3.3 主成份灰關聯分析 26
3.4 智慧型參數設計 26
第四章 實驗規劃 29
4.1 實驗準備 29
4.2 推力量測規範 32
4.3 推力試驗數據 33
第五章 製程參數優化 37
5.1 主成份分析法 37
5.2 主成份灰關聯分析 39
5.3 類神經智慧型參數設計 41
5.4 最佳製程參數 45
5.5 確認實驗 46
5.6 效益分析 47
第六章 結論與建議 49
6.1 實驗結論與討論 49
6.2 研究貢獻 49
6.3 未來展望與後續研究建議 50
參考文獻 52


表目錄
表4.1 IR180膠水熱固條件 30
表4.2控制因子水準 30
表4.3 L9(34)直交表 32
表4.4室溫量測直推力數據 34
表4.5室溫量測橫推力數據 34
表4.6高溫量測直推力數據 35
表4.7高溫量測橫推力數據 35
表5.1標準化後之SN比 37
表5.2相關係數矩陣 38
表5.3特徵值及寄予率 38
表5.4各主成份之特徵向量 38
表5.5多品質特性指標 39
表5.6主成份分析之最適參數組合39
表5.7主成份得點差序列 40
表5.8灰關聯度 40
表5.9主成份分析結合灰關聯之最適參數組合41
表5.10各品質特性尺度化後數值 42
表5.11類神經網路最適參數組合 44
表5.12各方法最適參數水準組合 45
表5.13各方法建議之最適參數組合條件下生產之樣本實驗數據46
表5.14確認實驗 47
表5.15驗證實驗統計 47
表5.16傳統製程及新製程相關成本彙整48


圖目錄
圖1.1研究流程圖 6
圖3.1直交表符號 19
圖3.2參數設計流程 21
圖3.3倒傳遞類神經網路示意圖 28
圖4.1音響產品PCBA 30
圖4.2點膠機 30
圖4.3點膠位置 31
圖4.4各條件下之低溫錫膏迴焊曲線(輸送帶速度-熱固溫度) 31
圖4.5量測0805陶瓷電容元件位置圖 32
圖4.6推力方向 33
圖4.7烘烤箱 33
圖4.8高溫推力量測 33
圖4.9各品質特性參數水準之反應圖 36
圖5.1神經網路架構 43
圖5.2網路學習指標 43
圖5.3合成單一品質特性神經架構圖 44



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