在工具機產業中，生產方式屬於大規模生產，因此在製造過程中保持高品質的產品至關重要，製造過程中的變異會對產品品質有重大的影響。一般而言，會需要即時監控設備加工產品的狀態，因此本研究為開發出線上即時監控的品管系統，達到降低變異與提生產量等目標。

本研究透過資料科學的架構結合統計製程管制開發了多個管制圖，並考量其功能與限制互相配合。首先，產品樣本透過分群分析將資料分為許多組，並將每一群當為產品的基準，其次，根據相似的樣本建構單變量管制圖。過程中製程資料的中心線會隨時間不斷改變，因此我們使用基於輪廓監督與時間序列的方式建構管制圖。最後我們以多變量管制圖Hotelling’s T2管制圖與基於核距離(Kernel distance)的管制圖進行比較。

為了驗證我們提出的研究架構，我們對工具機案例公司進行實證研究。結果表明，透過資料探勘與機器學習技術進而使用統計製程管制建構管制圖的這一套流程，依此流程建構的品管系統顯著提高了管制圖監控的功能與可靠性，進而可以提升產量。

In machine tool industry, it’s critical to maintain high-quality products during the manufacturing process while the variance in the process shows significantly impact on quality issue, especially in mass production. Typically, it’s necessary to monitor the real-time status of equipment control; that is, it’s urgent to develop a production system which has real-time monitoring for driving productivity and reducing variance.

This study developed several statistical control charts via data mining framework. First, data was divided into many groups by cluster analysis, and the baseline of product can be defined for each cluster. Second, univariate control charts were developed based on the baselines of qualified products. In particular, the control charts were based on time series and profile monitoring since the monitored target will constantly change over time. Finally, we also extended to develop the multivariate control chart with Hotelling’s T2 control chart and kernel distance control chart for the comparison.

An empirical study of machine tool manufacturer was conducted to validate the proposed framework. The result shows that the quality system embedded with data mining and machine learning technology significantly enhanced the monitoring reliability of control charts and improved the yield.

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