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dc.contributor.author吉田健太zh_TW
dc.contributor.author唐麗英zh_TW
dc.contributor.authorYoshida, Kentaen_US
dc.contributor.authorTong, Lee-Ingen_US
dc.date.accessioned2018-01-24T07:41:14Z-
dc.date.available2018-01-24T07:41:14Z-
dc.date.issued2017en_US
dc.identifier.urihttp://etd.lib.nctu.edu.tw/cdrfb3/record/nctu/#GT070553359en_US
dc.identifier.urihttp://hdl.handle.net/11536/141644-
dc.description.abstractzh_TW
dc.description.abstractRecent technological advances in manufacturing industry allow high-speed mass production and manufacturers often utilize the online inspection machines to monitor the production process. However, the online inspection machines for image data in a high-speed mass production process are still in the development stage. They might cause misclassifications of the non-defective or defective products, and therefore, result in unnecessary process adjustment or profit loss. Previous studies developed some high accurate discriminant methods such as feature extraction in relation to a filtering window, and ensemble classifiers for identifying the defective products. However, these methods have some drawbacks which may cause high misclassification rates when they are employed in a high-speed mass production process. Hence, the objective of this study is to build a more precise defect detection system to discriminate the non-defective products and defective products using the convolutional neural network and experimental designs. The proposed method is verified to be superior to the existing defect detection system for classifying the image data in a high-speed mass production process.en_US
dc.language.isoen_USen_US
dc.subject品質管理zh_TW
dc.subject缺陷偵測zh_TW
dc.subject卷積神經網絡zh_TW
dc.subject影像處理zh_TW
dc.subject實驗設計zh_TW
dc.subject高速生產zh_TW
dc.subjectQuality controlen_US
dc.subjectDefect detectionen_US
dc.subjectConvolutional neural networken_US
dc.subjectImage processingen_US
dc.subjectExperimental designsen_US
dc.subjectHigh-speed productionen_US
dc.title應用卷積神經網絡和實驗設計改善高速大量製程圖像缺陷偵測系統zh_TW
dc.titleImproving the Defect Detection for Image Data in a High-speed Mass Production Process using Convolutional Neural Network and Experimental Designsen_US
dc.typeThesisen_US
dc.contributor.department工業工程與管理系所zh_TW
Appears in Collections:Thesis