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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 洪一薰 | |
| dc.contributor.author | Sheng-Chieh Chen | en |
| dc.contributor.author | 陳聖捷 | zh_TW |
| dc.date.accessioned | 2021-06-17T02:12:53Z | - |
| dc.date.available | 2023-01-04 | |
| dc.date.copyright | 2018-01-04 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-12-14 | |
| dc.identifier.citation | Chen, J. C. (1997). The study of dyeing condition for normal pressure dyeable polyester fiber. China Textile Institute, 7(3), 188-195.
Etemadifar, A., Dehghanizadeh, H., & Nasirizadeh N.and Rohani-Moghadam, M. (2014). Statistical optimization of wool dyeing with alizarin red s as a natural dye via central composite design. Fibers and Polymers, 15(2), 254-260. Gill, P. E., & Wong, E. (2012). Sequential quadratic programming methods. In Mixed integer nonlinear programming, Springer New York., 147-224. Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine learning, 3(2), 95–99. Guo, G. Y., & Chen, Y. L. (1994). Improving the dyeability of cotton with reactive dyes. American Dyestuff Reporter, 83(9), 58-67. Harada, N., & Yoshida, T. (1992). Recent developments in optimizing reactive dyeing of cotton. Textile Chemist and Colorist, 24(9). Holland, J. H. (1975). Adaptation in natural and artificial systems. an introductory anal- ysis with application to biology, control, and artificial intelligence. Ann Arbor, MI: University of Michigan Press. Huang, K. S., & Yen, M. S. (1997). Pregrafting influences on the dyeing properties of cotton fabrics. American Dyestuff Reporter, 86(4). Ibrahim, N. A., Youssef, M. A., Helal, M. H., & Shaaban, M. F. (2003). Exhaust dye- ing of polyester based textiles using high temperature alkaline conditions. Applied Polymer Science, 89(13), 3563-3573. Jasper, W. J., Kovacs, E. T., & Berkstresser, G. A. (1993). Using neural networks to predict dye concentrations in multiple-dye mixtures. Textile Research Journal, 63(9), 545-551. JY, L., & CK, L. (2007). Dyeing optimization with metal complex acid dyes for nylon fabrics. Journal of Textile Engineering, 53(3), 117–122. Nocedal, J., & Wright, S. J. (2006). Sequential quadratic programming. Springer New York., 529-562. Palamutcu, S. (2010). Electric energy consumption in the cotton textile processing stages. Energy, 35(7), 2945-2952. Perez, R. E., Jansen, P. W., & Martins, J. R. (2012). pyopt: a python-based object-oriented framework for nonlinear constrained optimization. Structural and Multidisciplinary Optimization, 45(1), 101–118. Rainville, D., Fortin, F.-A., Gardner, M.-A., Parizeau, M., Gagné, C., et al. (2012). Deap: A python framework for evolutionary algorithms. In Proceedings of the 14th annual conference companion on genetic and evolutionary computation (pp. 85–92). Ritzel, B. J., Eheart, J. W., & Ranjithan, S. (1994). Using genetic algorithms to solve a multiple objective groundwater pollution containment problem. Water Resources Research, 30(5), 1589–1603. Wang, X., & Bide, M. (1998). Factors affecting the levelness of dyeing in reused acid dyebaths for nylon. Textile Chemist and Colorist, 30(4). Wu, C. C., & Chang, N. B. (2003). Global strategy for optimizing textile dyeing manufacturing process via ga-based grey nonlinear integer programming. Computers and Chemical Engineering, 27(6), 833-854. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/68115 | - |
| dc.description.abstract | 本研究的主要目的為，在已知染色製程參數以及染色表現因子的關 聯下，建構機能布染色製程參數優化模型，並考量生產成本以及品質成本，以達到有效降低成本以及提高品質的穩定程度。本研究主要分成二個部分，一部分探討染整製程參數優化技術，由表現因子關聯分析所估計的製程參數，建構成本相關的染整製程總成本模型、品質相 關的染整製程穩定度模型，以及與品質標準有直接關係的染整製程對 色差異度模型。另一個部分基於前面部分所建構的模型，本研究以非 線性規劃的 Sequential Quadratic Programming (SQP) 方法，分別對三個非凸規劃問題的模型，求解非線性規劃問題，藉由搜尋方法找出製程運作成本最低、品質穩定度最高以及對色差異度最小的表現因子組合。 | zh_TW |
| dc.description.abstract | This research is to develop the dyeing parameter optimization model for functional textiles based on the analysis of relationship between the manufacturing parameters in the dyeing process and the dyeing performance. The aim of this research is to minimize the total dyeing cost including the production and energy quality costs with the consideration of robustness measure and dyeing performance. Based on the relationship the estimated process parameters, the first task is discussion of the dyeing parameter optimization for dyeing process total costs model, dyeing process robustness model with consideration of the quality and the quality model about the chromatic aberration. According to the models, we use Sequential Quadratic Programming (SQP) method to solve the non-convex programming problems and search the combination of the maximum robustness of quality and the minimum of process cost and chromatic aberration. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T02:12:53Z (GMT). No. of bitstreams: 1 ntu-106-R04546020-1.pdf: 2236751 bytes, checksum: 6ef11268702319d826317342a5de1c34 (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | 誌謝............................................. ii
摘要............................................. iii Abstract........................................... iv 目錄............................................. vi 圖目錄............................................ vii 表目錄. . . . . . . . . . . . . . . . . . . . . . . . . viii 第一章 紡織染色製程背景介紹............................ 1 第二章 文獻探討..................................... 3 2.1 染色製程參數與對色關聯性 ....................... 3 2.2 染色製程參數優化技術.......................... 4 第三章 紡織染色製程現況............................... 6 3.1 染色製程描述 ............................... 7 3.2 現況問題描述 ............................... 9 第四章 染整製程模型建立............................... 11 4.1 製程參數因子描述............................. 11 4.2 模型假設與符號表示 ........................... 14 4.3 模型建立.................................. 15 第五章 染整製程參數優化方法............................ 19 5.1 模型描述.................................. 19 5.2 非線性最佳化方法............................. 21 5.3 染整製程模型求解............................. 25 第六章 數值分析..................................... 30 6.1 模型數值化................................. 30 6.2 染整製程數值求解............................. 34 6.3 方法比較.................................. 36 6.3.1 基因演算法概述.......................... 36 6.3.2 基因演算法架構及求解...................... 37 6.4 最佳化參數組合與其他業界常用組合比較 ............... 40 第七章 結論....................................... 43 參考文獻.......................................... 44 | |
| dc.language.iso | zh-TW | |
| dc.subject | 非線性規劃 | zh_TW |
| dc.subject | 非凸集合 | zh_TW |
| dc.subject | 染色製程 | zh_TW |
| dc.subject | 穩定度 | zh_TW |
| dc.subject | 序列二次規劃 | zh_TW |
| dc.subject | SQP | en |
| dc.subject | dyeing process | en |
| dc.subject | robust | en |
| dc.subject | non-convex | en |
| dc.subject | nonlinear | en |
| dc.title | 紡織染色製程最佳化問題之研究 | zh_TW |
| dc.title | The Research of Parameters Optimization In a Textile Dyeing Process | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 藍俊宏,林仁彥,蔡坤成 | |
| dc.subject.keyword | 非凸集合,非線性規劃,序列二次規劃,穩定度,染色製程, | zh_TW |
| dc.subject.keyword | non-convex,nonlinear,SQP,robust,dyeing process, | en |
| dc.relation.page | 45 | |
| dc.identifier.doi | 10.6342/NTU201704464 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2017-12-14 | |
| 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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