使用最大概似估計每台機器的良率以識別批量生產系統中之低良品率機器

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姓名

費利普(Philip Faster Eka Adipraja) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

使用最大概似估計每台機器的良率以識別批量生產系統中之低良品率機器
(Per-Machine Yield Estimation Using Maximum Likelihood to Identify Low-Yield Machines on the Batch Production System)

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摘要(中)

偵測識別批量生產系統中的低良率機器對於最大限度地減少缺陷輸出至關重要。然而，解決這個複雜問題的最新方法需要大量的專業知識、昂貴的資源或兩者的結合。為了解決這個挑戰，我們提出了一種使用「Maximum Likelihood Estimation」和「Bootstrap Confidence Intervals」的簡單且經濟高效成本效益高的方法。此方法可以有效估計每台機器的良率，從而能夠精確定位低良率機器並產生優先順序清單以進行進一步調查。擁有 50-500 台機器的製造商可以透過建立包含約 6-20 倍生產機器批次的資料集來實施此方法。當滿足此條件時，系統可有效偵測識別最多 5 台低良率機器。此外，估計的每台機器良率可用於預測各個生產批次的良率，為生產計劃和優化提供有價值的見解。

摘要(英)

Identifying low-yield machines in batch production systems is crucial to minimize defective outputs. However, recent methods of addressing this complex issue require considerable expertise, expensive resources, or a combination of both. To solve this challenge, we propose a straightforward and cost-effective method using maximum likelihood estimation and bootstrap confidence intervals. This method efficiently estimates per-machine yield, enabling the pinpointing of low-yield machines and the generation of a prioritized list for further investigation. Manufacturers with 50–500 machines can implement this method by building a dataset containing approximately 6-20 times as many batches as production machines. When this condition is met, the system effectively identifies up to 5 low-yield machines. Additionally, the estimated per-machine yield can be used to predict the yield of individual production batches, providing valuable insights for production planning and optimization.

關鍵字(中)

★ 大量批量生產
★ 期望最大化演算法
★ 低良率機器
★ 機器維護建議
★ 每台機器良率估算

關鍵字(英)

★ batch production
★ expectation-maximization algorithm
★ low-yield machine
★ machine maintenance suggestion
★ per-machine yield estimation

論文目次

中文摘要 i
English Abstract ii
Acknowledgments iii
Table of Contents iv
List of Figures vi
List of Tables vii
Explanation of Symbols viii
Chapter I Introduction 1
1-1 A cornerstone objective for manufacturers 1
1-2 Challenges in batch production system 2
1-3 Per-machine yield estimation 4
1-4 Formal problem definition 5
1-5 Contributions 6
1-6 Research scopes and assumptions 6
Chapter II Related Work 8
2-1 Process-oriented and product-oriented diagnosis 8
2-2 Previous study on per-machine yield estimation 11
2-3 Advantages and limitations of the EM algorithm 13
Chapter III Proposed Method 14
3-1 Estimating per-machine yield 14
3-1-1 Maximum-likelihood estimation of per-machine yield 14
3-1-2 Steps of per-machine yield estimation 16
3-1-3 Complete equations of EM-based algorithm 18
3-1-4 Manual calculation example of EM-based algorithm 24
3-1-5 Bootstrap method 31
3-2 Generating a list of low-yield machines 32
3-3 Predicting per-batch yield 35
Chapter IV Simulation Experiments 37
4-1 Simulation process 37
4-2 Simulation example of a specific scenario 39
4-3 Results of low-yield machine identification 40
4-4 Results of per-batch yield predictions 43
Chapter V Discussion 46
5-1 Experimental results using the T-Company dataset 46
5-2 Practical implications 49
5-3 Limitations of the proposed method 51
Chapter VI Conclusion and Future Direction 54
6-1 Conclusion 54
6-2 Future work 54
References 56

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指導教授

梁德容(Deron Liang)

審核日期

2024-6-13

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