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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 洪一薰(Yi-Hsun Hong) | |
dc.contributor.author | Jhih-Hua Teng | en |
dc.contributor.author | 鄧治華 | zh_TW |
dc.date.accessioned | 2021-06-17T08:25:53Z | - |
dc.date.available | 2024-08-19 | |
dc.date.copyright | 2019-08-19 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-08-12 | |
dc.identifier.citation | Abdel-Azim, G., & Nasri, S. (2013, January). Textile defects identification based on neural networks and mutual information. In 2013 International Conference on Computer Applications Technology (ICCAT) (pp. 1-6). IEEE.
Adorni, G., Bianchi, D., & Cagnoni, S. (1998, May). Ham quality control by means of fuzzy decision trees: A case study. In 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No. 98CH36228) (Vol. 2, pp. 1583-1588). IEEE. Chien, C. F., & Chen, L. F. (2008). Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems with applications, 34(1), 280-290. Feigenbaum, A. V. (1951). Quality control: Principles, practice and administration: An industrial management tool for improving product quality and design and for reducing operating costs and losses. McGraw-Hill. Gloy, Y. S., Sandjaja, F., & Gries, T. (2015). Model based self-optimization of the weaving process. CIRP Journal of Manufacturing Science and Technology, 9, 88-96. [11] Abdel-Azim, G., & Nasri, S. (2013, January). Textile defects identification based on neural networks and mutual information. In 2013 International Conference on Computer Applications Technology (ICCAT) (pp. 1-6). IEEE. Ilangkumaran, M., & Kumanan, S. (2009). Selection of maintenance policy for textile industry using hybrid multi-criteria decision making approach. Journal of Manufacturing Technology Management, 20(7), 1009-1022. Park, S., Yun, C., Kim, J., & Park, C. H. (2013). The effects of the fabric properties on fabric movement and the prediction of the fabric movements in a front-loading washer. Textile Research Journal, 83(11), 1201-1212. Shewhart, W. A. (1931). Economic control of quality of manufactured product. ASQ Quality Press. Song, Y. Y., & Ying, L. U. (2015). Decision tree methods: applications for classification and prediction. Shanghai archives of psychiatry, 27(2), 130. Tyagi, S. K., & Sharma, B. K. (2011). Data mining tools and techniques to manage the textile quality control data for strategic decision making. International Journal of Computer Applications, 13(4), 26-29. Yang, S., & Gordon, S. (2017). Accurate prediction of cotton ring-spun yarn quality from high-volume instrument and mill processing data. Textile Research Journal, 87(9), 1025-1039. Yildirim, P., Birant, D., & Alpyildiz, T. (2017). Discovering the relationships between yarn and fabric properties using association rule mining. Turkish Journal of Electrical Engineering & Computer Sciences, 25(6), 4788-4804. Zhang, B., Wei, Z., Ren, J., Cheng, Y., & Zheng, Z. (2018). An empirical study on predicting blood pressure using classification and regression trees. IEEE access, 6, 21758-21768. 洪紹綺 (民107). 具有多對多資料特性之紡織製程瑕疵肇因分析. 台灣大學工業工程學研究所學位論文, 1-72. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74241 | - |
dc.description.abstract | 在製造業中,瑕疵品往往會導致大量的成本開銷,所以如何運用品質管理來改善生產流程,進而使良率最大化將會是一大議題。尤其是品管方法相對較為落後的傳統產業,往往只能靠在場人員的經驗來進行管理,久而久之將會導致許多問題產生。本研究將會結合資料探勘以及迴歸分析的手段,使用決策樹為基本模型,進而對紡織工廠的生產歷史資料進行分析,找出最佳的生產路徑,並透過分類參數的方式來進行生產參數設計。最後也將使用統計手法驗證模型的可靠度,測試在其設計下是否能能將產品的良率有效的提高。 | zh_TW |
dc.description.abstract | In a manufacturing industry, defected products often lead to a lot of cost. How to use quality management to improve the production process and maximize the yield is a big issue. Generally, traditional industries with relatively poor quality control methods often adjust parameters by the experience of the personnel present, which leads to many problems over time. This thesis combines data mining and regression analysis to analyze the past data of a textile factory, and use the decision tree as basic model to find the best production path. Furthermore, we design parameters by means of parameters classification. At last, statistical methods are used to verify the reliability of our model, and test whether the best production path can effectively improve the yield or not. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T08:25:53Z (GMT). No. of bitstreams: 1 ntu-108-R06546024-1.pdf: 1996417 bytes, checksum: d417f6583785a2cf7f3226f45655b375 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 圖目錄 v
表目錄 vi 第一章 緒論 1 第二章 文獻回顧 5 第三章 迴歸分析與決策樹 8 3.1 資料處理 8 3.2 模型架構 17 3.2.1 迴歸樹構成與建構 17 3.2.2修剪樹狀結構 20 3.2.3迴歸樹的意義解釋與應用方法 20 第四章 個案研究 22 4.1 資料取樣 22 4.2 實驗結果 25 4.2.1 二元迴歸樹呈現 25 4.2.2 測試驗證結果 26 4.2.3 個別瑕疵討論 28 4.3 參數設計 30 第五章 結論 33 參考文獻 36 | |
dc.language.iso | zh-TW | |
dc.title | 多階段織造流程品質改善研究 | zh_TW |
dc.title | Quality Enhancement of the Multi-stage Weaving Process | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳文智(Wen-Chih Chen),吳政鴻(Cheng-Hung Wu),藍俊宏(Chun-Hung Lan) | |
dc.subject.keyword | 品質管理,生產參數設計,資料探勘,決策樹,統計驗證, | zh_TW |
dc.subject.keyword | Quality management,Production parameter design, Data mining,Decision tree,Statistical verification, | en |
dc.relation.page | 37 | |
dc.identifier.doi | 10.6342/NTU201902890 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-08-13 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工業工程學研究所 | zh_TW |
顯示於系所單位: | 工業工程學研究所 |
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