結合廻歸模糊與田口方法發展表面粗糙度預測系統之研究

A Study of Surface Roughness Prediction System using Fuzzy Regression and Taguchi Method

10.6840/CYCU.2010.00465

呂朋樺

模糊理論 ； 表面粗糙度 ； 田口法 ； 迴歸分析 ； Regression Analysis ； Surface Roughness ； Fuzzy Theory ； Taguchi Method

中原大學工業與系統工程研究所學位論文

2010年

碩士

黃博滄

繁體中文

智慧型控制理論在現代被廣泛的研究與應用在各領域中，且在各領域中都有令人激賞的表現，但隨著科技日新月異的進步，現今智慧型控制系統越趨複雜及目標難以精確化的問題就此產生，此時模糊理論的誕生解決了此相關方面的問題。 由於模糊理論擁有能夠處理複雜與目標難以精確化的特性，現今己活躍在各種領域當中，但是在建置模糊系統的過程中將會遇到兩個主要的困難點，第一點為定義模糊IF-THEN規則庫中適當歸屬函數；第二點為找尋最佳的模糊歸屬函數個數組合，這兩點是在模糊系統建置過程中一定會遇到的困難，故本研究將針對模糊建置上會遇到的兩個主要困難做有效及正確的改善，運用迴歸分析建立廻歸模組來輔助定義出模糊IF-TEHN規則庫，與田口法優化模糊歸屬函數個數，找出最佳歸屬函數個數組合，並且將兩者結合於模糊預測系統中，建置一套更有效且更準確的模糊預測系統。 為證明本研究所提出之方法的有效性與準確性，將所發展之結合迴歸模糊與田口法導入至表面粗糙度預測的實例中，建置出表面粗糙度預測系統，證明出此預測系統能有效且準確的預測出表面粗糙度，並且提升了原模糊系統的準確性。最後運用迴歸分析做表面粗糙度的預測，來驗證出結合迴歸模糊與田口法所發展的預測系統比迴歸預測方法還具有效性及準確性。

Intelligent control theory has been studied in modern research and widely applied in various fields. With the rapid technological advances, however, intelligent control system becomes more complex and is difficult for researchers to define accurately. As a result, fuzzy theory is proposed to solve problems in the relevant areas. Although the fuzzy theory can be used to solve complex issues and make accurate definitions, two main issues occur in the process of the building fuzzy systems including defining appropriate membership functions in the fuzzy IF- THEN rule bank and searching the best combined number pairs in the fuzzy membership functions. In order to handle these two issues, the current study adopted regression analysis to define the fuzzy IF-TEHN rule bank, and membership functions of Taguchi Method to search the best combined number pairs in the fuzzy system. Then the two are combined to build an effective and accurate fuzzy prediction system. The fuzzy prediction system proposed in the study was used to predict surface roughness for verifying its effectiveness and accuracy. The method composed of fuzzy regression and Taguchi Method was developed, and was proved to accurately predict surface roughness and improve predictions of the original fuzzy system. Finally, the study testified that the method composed of fuzzy regression and Taguchi Method has a better and more accurate prediction compared to regression analysis.

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