多階段織造流程品質改善研究

Abstract

在製造業中，瑕疵品往往會導致大量的成本開銷，所以如何運用品質管理來改善生產流程，進而使良率最大化將會是一大議題。尤其是品管方法相對較為落後的傳統產業，往往只能靠在場人員的經驗來進行管理，久而久之將會導致許多問題產生。本研究將會結合資料探勘以及迴歸分析的手段，使用決策樹為基本模型，進而對紡織工廠的生產歷史資料進行分析，找出最佳的生產路徑，並透過分類參數的方式來進行生產參數設計。最後也將使用統計手法驗證模型的可靠度，測試在其設計下是否能能將產品的良率有效的提高。

In a manufacturing industry, defected products often lead to a lot of cost. How to use quality management to improve the production process and maximize the yield is a big issue. Generally, traditional industries with relatively poor quality control methods often adjust parameters by the experience of the personnel present, which leads to many problems over time. This thesis combines data mining and regression analysis to analyze the past data of a textile factory, and use the decision tree as basic model to find the best production path. Furthermore, we design parameters by means of parameters classification. At last, statistical methods are used to verify the reliability of our model, and test whether the best production path can effectively improve the yield or not.