無線通訊產業WLAN在全球化競爭下，面臨高品質低成本之要求，如何在維持品質甚至提昇品質之同時提降低成本，成為急需解決之問題。無線通訊產業WLAN之生產過程，除SMT、 DIP生產製程及組裝製程外，尚包括大量的檢測，通常以全檢來確保產品出廠品質，尚包括大量的檢測，通常以全檢來確保產品出廠品質，若能減少不必要的檢查過程和資源，則有助於提昇競爭力，本研究發展大數據分析之應用架構，以改善WLAN之生產及檢測過程。
本研究以改善製程檢測問題進行探討，設定產品為訂貨生產進行模式下建構大數據探勘架構，透過資料面的探索與挖掘來協助工程師更有效率找出的檢測站之間關聯因素。以台灣某無線通訊產業WLAN之生產線製程檢測資料進行架構測試，在維持品質之前提下，本研究以關聯規則方法找出不同檢測項目間之關聯性，減少所需檢測次數；接著再以決策樹分析找出最適之可控因子水準（如各製程及檢測站之溫度、濕度等）以改善品質。最後，以實證資料進行規則之驗證。步驟包括蒐集檢測缺陷、檢測項目與機台製程參數等資訊，進行資料預處理工作，第一部份採用動態式關聯規則進行資料分析，透過設定門檻値篩選規則，萃取檢測項目之間相互影響因素，第二部份以動態式決策樹分析影響檢測效率之原因，找出較佳參數配置並提供給工程師作為參考依據。萃取檢測站之間相互影響因素。以個案公司之實證資料並採用本研究提出之大數據分析架構，針對60個檢測項目，以6223筆生產資料萃取出檢測站間10筆關聯規則及5棵決策樹，再以4526筆生產資料進行驗證。結果顯示，在驗證的4256台產品中，可減少16.2%的檢測次數，決策樹所建議之可控因子水準為環境的溫度與濕度，改善濕度與溫度設置能夠在不增加成本情況下提升品質。由實證結果可得知，本研究所提出之大數據分析應用架構具可行性，可為實務所應用，且降低時間成本與設備成本，建立一套以無線通訊產業為例之製程檢測效率提昇的模型架構，以及未來研究方向以整合機器學習或其他模型更進一步發展。

Wireless communication industry WLAN is under a very competitive market. To reduce the cost while maintaining quality is always a critical issue in this industry. In addition to the SMT, DIP production process and assembly process, the critical production processes of WLAN industry also include a large number of inspections. While inspection is always a necessary evil, there is a strong need to reduce the unnecessary inspection processes and resources to improve the competitiveness.
This work develops a framework to apply big data analytics to improve WLAN production and inspection processes. A case company is selected and a data-warehouse is established for a specific lot of products. After the data pre-processing work, the relationships among the detection stations and the detection results could be found. Various meta-models are used, which including association rule method to find the correlation between different test items and to reduce the number of required test; the decision tree to find the optimal controllable factor, and other potential methods. Finally, the developed rules are further verified by empirical data.
In this specific case, there are 6223 pieces of production data and 60 test items, where 10 pieces of association rules and 5 decision trees from the test stations are extracted. Once the rules from the models are developed, 4526pieces of data is used to validate our findings. It is concluded from this empirical study that the number of detections could be reduced by around 16.2%, a huge number of cost saving. It is also found out that the critical control factors, recommended by the decision tree, are temperature and humidity. The proposed big data application framework is therefore feasible in this case and machine learning or other models could be further extended, which is our future research direction.

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IBM https://www.ibm.com/news/tw
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Apache Hadoop官方網站 http://hadoop.apache.org