臺灣博碩士論文加值系統

現今，品質是最佳的競爭優勢，所以高品質的生產也變的相當重要。品質控制
是一個確保產品或服務維持一定水準的程序。其中一項品質控制的技術是從產品的
屬性來進行品質預測。然而，傳統的品質控制方法有些弱點，像是特定的控制限制
與不確定性資料分析等。為了改善品質控制的效率，本研究提出一個以代理人為基
礎的複合式方法包括約略集合理論、模糊邏輯與遺傳演算法。在這個代理人為基礎
的系統中，每個代理人是用來提供一個或多個功用。屬性與法則擷取階段是使用約
略集合理論擷取出顯著屬性與法則。品質預測階段是分析這些顯著屬性，並依照其
歸屬函數與模糊法則來發展模糊邏輯系統以進行產品品質預測。最後，最佳化階段
再協助模糊邏輯系統尋求最佳的解。

Nowadays quality is one of the best sources of competitive advantage. High quality performance is becoming of critical importance. Quality control is a process employed to ensure a certain level of quality in a product or service. One of the techniques in quality control is to predict the product quality abased on the product features. However, traditional quality control techniques have some weaknesses such as specific control limits, heavily on the collection and analysis of data and uncertainty processing. In order to promote the effectiveness of quality control, an agent-based hybrid approach incorporated with the rough set theory (RST), fuzzy logic and genetic algorithm is proposed in this thesis. In this agent-based system, each agent is able to perform one or more functionality in three stages: The feature & rule extraction stage is a RST procedure which used to extract significant features and decision rules. The quality prediction stage is used to develop a FLS to predict machining part quality. The optimization stage is to search the optimal solution of the FLS.

誌謝 i
摘要 ii
Abstract iii
List of Contents iv
List of Figures vi
List of Tables vii
1. Introduction 1
2. Literature review 6
2.1 Quality control of machining process 6
2.2 Intelligent agent 11
2.3 Rough set theory 14
2.4 Fuzzy logic system 17
2.5 Genetic algorithms 21
2.6 Summary 23
3. Solution approach 24
3.1 The Agent-based hybrid approach 24
3.1.1 Environment of intelligent agent system 24
3.1.2 The agent architecture 28
3.2 The feature & rule extraction stage and its solution procedure 31
3.2.1 The reduct generation agent 32
3.2.2 The rule extraction agent 34
3.2.3 The rule-validation agent 37
3.3 The quality prediction stage and its solution procedure 39
3.3.1 The fuzzifer agent and rule determination agent 40
3.3.2 The inference agent and defuzzification agent 43
3.4 The optimization stage and its solution procedure 46
4. A case study 50
4.1 The feature & rule extraction stage and solutions 56
4.1.1 The RST Software 56
4.1.2 Computational result of feature & rule extraction stage 56
4.2 The quality prediction stage and solutions 59
4.2.1 The fuzzifer and rule determination agent 60
4.2.2 Fuzzy logic system software 62
4.3 Evaluation of Type I and Hybrid FLS using GA agent 62
4.4 Discussion 66
5. Conclusion and further studies 68
References 70

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