隨著製造技術日益進步，以及資訊科技的蓬勃發展，面對瞬息萬變且競爭激烈的市場，企業須有效提升產品品質並降低成本，釐清影響製程之關鍵，以提升競爭力。然而，單憑工程知識或經驗法則已無法有效改善，因此本研究結合鑄造產業知識與資訊科技的應用，掌握關鍵的競爭優勢，進而提升企業產品品質。本研究應用資料探勘技術於鑄造業製程品質改善問題，提出一套屬性篩選流程，綜合類神經網路、隨機森林、支持向量迴歸、約略集合理論、迴歸分析等五種方法，彙整各方法篩選之重要變數，找出影響製程關鍵參數，並建構縮減模型。在確定關鍵製程參數後，結合類神經網路與基因演算法於製程參數最佳化，找出關鍵參數的最佳化參數組合。本研究以台灣某鑄造工廠為例，依照所提出之屬性篩選流程進行實證研究，將原先17項製程參數篩選出9項關鍵參數，並找出關鍵參數之最佳化參數組合。另外，比較五種資料探勘方法之縮減模型績效，結果顯示在屬性篩選後的縮減模型仍保有不錯的預測能力，說明本研究所提出屬性篩選流程之可行性。

With the advancement of manufacturing technology and the flourishing development of information technology, casting industry is faced with the increasingly competitive market, so companies must enhance their product’s quality and reduce manufacturing costs, and clarify what highly influences the process to have the key competitive advantage. However, simply relying on domain knowledge or rules of thumb is unable to identify the root causes of quality problems effectively.
This study applies data mining techniques for the process improvement issue of casting industry and proposes a general procedure for attribute selection. Five data mining techniques, including artificial neural network (ANN), random forest (RF), support vector machine (SVM), rough set theory (RST), and regression analysis are used to select the important attributes. This study aggregates the results from each method to identify the key parameters and builds the reduced model. In the end, the artificial neural network and genetic algorithm (GA) are utilized for optimizing the selected process parameters.The proposed procedure was employed to analyze the manufacturing data of a casting company in Taiwan. The research results presented that nine key process parameters were identified from seventeen original attributes and then the optimal combination of key parameters was obtained. In addition, the reduced model still maintained the exceptional ability to perform adequately, which confirmed the feasibility of the proposed attribute screening procedure.

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