臺灣博碩士論文加值系統

在印刷電路板組裝的製程中，部份元件的組裝已由自動化設備取代人力，但仍然有相當數量且種類繁多的雙列直插式封裝(Dual In-line Package, DIP)元件需藉由人工的方法組裝於印刷電路板上。DIP的組裝生產線由多個工作站所組成，每個工作站負責置放數個元件，而由於以人工方式置放元件，影響生產線的效率與組裝品質的因素，除了有傳統的生產線平衡問題(Assembly Line Balancing Problem, ALBP)外，還需考量人員在組裝過程的順暢度，以及為避免組裝錯誤而加入的組裝限制。本研究以DIP組裝線為研究對象，設計一套基因演算法(Genetic Algorithm, GA) ，在給定工作站數量與元件的指派限制之下，將所有元件指派於工作站中，使各工作站中的最大週期時間最小，並符合各項指派限制。本研究利用台灣某電腦主機板製造廠提供的產品資訊測試本研究所設計的演算法，結果發現本研究所設計的方法所產生元件指派方式，與該公司現行之指派方式相比，能有更短的最大週期時間，並更能符合該公司所設計的指派限制。本研究亦另行模擬了更複雜的產品資訊，在以本研究所提出的方法指派元件後，亦能順利地於短時間內找出能夠符合指派限制，並使最大週期時間最小的元件指派方式。

In the assembly line for printed circuit board assembly (PCBA), automation is a tool that is being used instead of using actual labor. However, while assembling Dual In-line Packages onto PCBs, actual labor is still required. While there are many tasks on the assembly line, assigning components is what affects the production efficiency on the assembly line. This is an Assembly Line Balancing Problem (ALBP). This research is conducted regarding assembling DIP’s onto PCB’s based on assigning the components onto a fixed number of assembly stations, in order to minimize the cycle time. This is a Type II Problem where this research applies Genetic Algorithm (GA) for solving. The GA contains constraints to be solved within a short time frame with respect to these constraints. This research uses both product information obtained from an anonymous motherboard manufacturer in Taiwan and also simulated product information in the GA. The outcome of this research met the constraints and minimized the cycle time.

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
參數對照表 viii
第一章 緒論 1
1.1研究背景與動機 1
1.2 研究目的 3
1.3 研究架構 4
第二章 文獻探討 5
2.1 雙列直插式封裝 5
2.2 生產線平衡 6
2.3 基因演算法 8
第三章 研究方法 12
3.1 PCBA現況與指派限制條件 12
3.1.1 PCBA現況 12
3.1.2相關名詞與參數定義 12
3.1.3 指派限制條件 13
3.2 PCB之DIP組裝數學模型 16
3.2.1 基本假設 16
3.2.2 數學模型 16
3.3 基因演算法 18
3.3.1 編碼 20
3.3.2 產生初始解 20
3.3.3 檢查並調整染色體為可行解 28
3.3.4 基因操作及其他基因參數 41
3.3.5 給定座標 45
第四章 實例驗證與分析 47
4.1 產品資訊 47
4.1.1 A公司產品資訊 47
4.1.2 模擬產品資訊 48
4.2 參數設定 50
4.2.1 適應函數權重分配 50
4.2.2 母體數及世代數設定 51
4.2.3 基因操作參數設定 51
4.3 指派結果與分析 53
4.3.4 實際產品指派結果與分析 54
4.3.2 模擬產品資訊指派結果與分析 58
第五章 結論與未來研究方向 65
5.1 結論與貢獻 65
5.2 未來研究方向 65
參考文獻 67

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