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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/70822完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張瑞益(Ray-I Chang) | |
| dc.contributor.author | Chia-Yun Lee | en |
| dc.contributor.author | 李佳芸 | zh_TW |
| dc.date.accessioned | 2021-06-17T04:39:49Z | - |
| dc.date.available | 2020-08-15 | |
| dc.date.copyright | 2018-08-15 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-08-07 | |
| dc.identifier.citation | [1] 紡拓會.(2016).台灣紡織工業概況。取自:http://www.textiles.org.tw/TTF/main/content/wHandMenuFile.ashx?file_id=1.
[2] Jeschke, S., Brecher, C., Meisen, T., Özdemir, D., & Eschert, T. (2017). Industrial internet of things and cyber manufacturing systems. In Industrial Internet of Things (pp. 3-19). Springer, Cham. [3] J. Bedrij, “Ca Hasanbeigi, A. (2010). Energy-efficiency improvement opportunities for the textile industry. [4] 蔡坤成. (2016). 織造製程品質提升與染色優化創新技術發展. 電工通訊季刊, 78-84. [5] Goswami, B. C., Anandjiwala, R. D., & Hall, D. (2004). Textile sizing. CRC press. [6] NPTEL Textile Engineering Fabric Manufacture - I (Web) L1: Weaving Technology (http://nptel.ac.in/courses/116102005/). [7] Dorgham ME (2013) Warping Parameters Influence on Warp Yarns Properties: Part 1: Warping Speed and Warp Yarn Tension. J Textile Sci Eng 3:132. doi: 10.4172/2165-8064.1000132. [8] Harada, N., & Yoshida, T. (1992). Recent Developments in Optimizing Reactive Dyeing of Cotton. Textile Chemist & Colorist, 24(9). [9] Lin, H., Wang, J., Long, A. C., Clifford, M. J., & Harrison, P. (2007). Predictive modelling for optimization of textile composite forming. Composites Science and Technology, 67(15-16), 3242-3252. [10] Hong, I. H., Shen, Z., Chen, S. C., Chen, A., Tsai, K. C., & Lin, Y. T. (2017). Manufacturing Parameters Optimization in Functional Textile Dyeing Processes. Procedia Manufacturing, 11, 619-624. [11] Legendre, A. M. (1805). Nouvelles méthodes pour la détermination des orbites des comètes. F. Didot. [12] Gauss, C. F. (1809). Theoria motus corporum coelestium in sectionibus conicis solem ambientium auctore Carolo Friderico Gauss. sumtibus Frid. Perthes et IH Besser. [13] Gauss, C. F. (1823). Theoria combinationis observationum erroribus minimis obnoxiae (Vol. 1). Henricus Dieterich. [14] Galton, F. (1989). Kinship and correlation. Statistical Science, 4(2), 81-86. [15] Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 267-288. [16] Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301-320. [17] Wang, S., Ji, B., Zhao, J., Liu, W., & Xu, T. (2017). Predicting ship fuel consumption based on LASSO regression. Transportation Research Part D: Transport and Environment. [18] Liu, H., & Motoda, H. (2012). Feature selection for knowledge discovery and data mining (Vol. 454). Springer Science & Business Media. [19] Arlot, S., & Celisse, A. (2010). A survey of cross-validation procedures for model selection. Statistics surveys, 4, 40-79. [20] Danielsson, P. E. (1980). Euclidean distance mapping. Computer Graphics and image processing, 14(3), 227-248. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/70822 | - |
| dc.description.abstract | 在工業4.0發展中，以資料科學的角度整合自動化實務和管理經驗，藉以改善製程品質與生產效能，是製造業轉型中重要的一環。本研究採用機器學習方式，對於不同紡織製品預測紡織梭織製程準備段的十五個機台操作參數，希望改善目前紡織產業大多需要依靠專家經驗進行相關判斷的缺點。本研究針對不同織品的紡織原料數據，利用特徵選取的方法找出關鍵特徵，並使用四種不同的迴歸分析模型對於紡織製程準備段參數進行預測，分析比對其正確率。交叉驗證實驗結果顯示，採用Linear Regression演算法的機器學習模型在梭織製程準備段的參數預測中表現最為優異，其均方誤差可縮小至0.056%。 | zh_TW |
| dc.description.abstract | Today, in an Industry 4.0 factory, improving quality of product and efficiency of manufacturing process are important goals in the textile industry. The utilization of advance-prediction tools, so that data can be systematically processed into information and thereby make more informed decisions. To achieve these goals, this study integrates automatic production process and management experience from the perspective of data science and provides a method using 4 different machine learning models to predict the machine operating parameters of the preparation section in fabric manufacturing. The purpose of this study is to intellectualize the textile manufacturing process in order to avoid the tech skills gap and also higher the efficiency of the textile manufacturing process. Based on 10-fold cross-validation, experimental results show that the proposed method provides good performance when comparing with previous stochastic methods, and the best regression model for predicting preparation section parameters can reduce the mean square error (MSE) to 0.056% | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T04:39:49Z (GMT). No. of bitstreams: 1 ntu-107-R05525062-1.pdf: 3840086 bytes, checksum: efde340a190ee6123ac336ea3b35fe9a (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTRACT iii 目錄 iv 圖目錄 vii 表目錄 ix Chapter 1 緒論 1 1.1 研究動機 1 1.2 目標與貢獻 2 1.3 論文架構 3 Chapter 2 問題介紹與文獻探討 4 2.1 紡織工業 4 2.1.1 紡織產業梭織製程 4 2.1.2 紡織製程參數相關研究 6 2.2 迴歸分析 6 2.2.1 機器學習訓練迴歸模型 7 2.2.2 線性迴歸模型 7 2.2.3 其他常見的迴歸模型 8 Chapter 3 研究方法 10 3.1 實驗架構 10 3.2 資料分析 10 3.2.1 資料搜集 11 3.2.2 資料分析工具 12 3.2.3 資料前處理 14 3.2.4 特徵選取 20 3.3 模型訓練 22 3.3.1 迴歸模型訓練 22 3.3.2 演算法步驟 23 Chapter 4 實驗結果 24 4.1 評估標準 24 4.2 模型效能比較 25 4.2.1 實驗一結果 25 4.2.2 實驗二結果 27 4.2.3 離群值對訓練結果的影響 30 4.2.4 演算法參數對模型的影響 31 4.2.5 預測結果轉多維度向量比對其距離 31 Chapter 5 結論與未來發展 33 5.1 研究貢獻 33 5.1.1 紡織梭織製程參數預測模型 33 5.1.2 將人工智慧導入傳統產業的效益 33 5.2 未來發展 34 參考文獻 35 附錄一 整經工序預測結果 37 附錄二 漿紗工序預測結果 39 附錄三 併經工序預測結果 41 附錄四 刪除離群值後的併經資料觀測結果 42 | |
| dc.language.iso | zh-TW | |
| dc.subject | 操作參數 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 迴歸分析 | zh_TW |
| dc.subject | Machine Learning | en |
| dc.subject | Regression Analysis | en |
| dc.subject | Operation Parameters | en |
| dc.title | 利用機器學習方法預測紡織準備段操作參數 | zh_TW |
| dc.title | Parameter Prediction for Preparation Section in Fabric Manufacturing by Machine Learning | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 蔡進發(Jing-Fa Tsai),張信宏(Shin-Hung Chang),蕭宇程(Yu-Cheng Hsiao) | |
| dc.subject.keyword | 機器學習,迴歸分析,操作參數, | zh_TW |
| dc.subject.keyword | Machine Learning,Regression Analysis,Operation Parameters, | en |
| dc.relation.page | 42 | |
| dc.identifier.doi | 10.6342/NTU201802652 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2018-08-07 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
| 顯示於系所單位： | 工程科學及海洋工程學系 | |
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