具整体学习机制之在制品品质检测暨缺陷分类技术全文PDF.PDF

具整體學習機制之在製品品質檢測暨缺陷分類技術 具整體學習機制之在製品品質檢測暨缺陷分類技術 WIP Quality Detection and Defect Classification based on Ensemble WIP Quality Detection and Defect Classification based on Learning Methods Ensemble Learning Methods 鄭光宏 夏啟峻 葉峻賓 江宜霖 林順傑 蔡煥文 鄭光宏 夏啟峻 葉峻賓 江宜霖 林順傑 蔡煥文 Kuang Hung Cheng, Chi-Chun Hsia, Jun-Bin Yeh, Yi-Lin Chiang, Shun-Chieh Lin, Huan-Wen Tsai Kuang Hung Cheng, Chi-Chun Hsia, Jun-Bin Yeh, Yi-Lin Chiang, Shun-Chieh Lin, Huan-Wen Tsai 中文摘要 產品品質是企業追求客戶滿意、提高競爭力的重要指標，生產檢測則是確保產品品質的重要 手段，然為節省成本及時間，目前業界採取抽檢方式來進行品質監測，但如此並無法確保每一個 產品品質 ，故透過資料分析手法來改善工廠之生產品質與效率是一重要課題。本研究所提出技術 採用機器學習方式進行大數據資料建模 ，包含支援向量機、邏輯斯迴歸及決策樹等各式分類器 ， 在普遍製造現場資料特性未知情形下，以袋式(Bagging) 整體學習(Ensemble Learning) 機制進行模 型訓練，有效降低傳統方法因選擇錯誤分類器而造成過適(overfitting) 風險，達成穩健的品質分類 效能 ，以低時間延遲方式，檢測產品的品質與良率，當不良品數目過高或良率不佳時，警示現場 作業人員進行必要的停機檢查 。導入本技術可幫助現場人員即時掌握在製品品質狀況，防杜廢品 進入下一到製程而產生之二次損失成本，同時確保產品品質 。 Abstract In order to gain customer satisfaction and enhance competitiveness, manufacturers usually aim to offer their clients with high quality products. However, under the consideration of time and cost reduction, random sampling has become a popular method for production quality metrology whereas not all the products will be examined and assured. To overcome this problem , this paper proposes a WIP Quality Detection and Classification method based on Ensemble Learning with well-known machine learning approaches, like Support Vector Machine (SVM), Logistic Regression, Decision Tree, and so on . Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble refers only to a concrete finite set of alternative models, but typical