纺织学报 ›› 2021, Vol. 42 ›› Issue (01): 46-52.doi: 10.13475/j.fzxb.20200501708

• 纺织工程 • 上一篇    下一篇

基于模糊多准则的涤纶低弹丝生产工艺参数优化

邵景峰1,2(), 李宁1, 蔡再生2   

  1. 1.西安工程大学 管理学院, 陕西 西安 710048
    2.东华大学化学化工与生物工程学院, 上海 201620
  • 收稿日期:2020-05-07 修回日期:2020-09-21 出版日期:2021-01-15 发布日期:2021-01-21
  • 作者简介:邵景峰(1980—),男,教授,博士。研究方向为智能信息处理。E-mail: shaojingfeng1980@aliyun.com
  • 基金资助:
    陕西省重点研发计划项目(2020GY-122);陕西省教育厅服务地方科学研究项目(20JC013);西安市科技计划项目(2020KJRC0018);西安市碑林区科技计划项目(GX1905);西安工程大学研究生创新基金项目(chx2020021)

Parameters optimization on polyester drawn textured yarn based on fuzzy multi-criteria

SHAO Jingfeng1,2(), LI Ning1, CAI Zaisheng2   

  1. 1. School of Management, Xi'an Polytechnic University, Xi'an, Shaanxi 710048, China
    2. College of Chemistry,Chemical Engineering and Biotechnology, Donghua University, Shanghai 201620, China
  • Received:2020-05-07 Revised:2020-09-21 Online:2021-01-15 Published:2021-01-21

摘要:

为解决涤纶低弹丝生产工艺参数间存在强耦合的问题,首先分析了涤纶低弹丝生产过程中的碳排放信息,对工艺参数之间的强耦合关系进行解析,设计了基于信噪比的工艺参数关系正交试验方案。然后在数据预处理的基础上,构建基于模糊多准则的工艺参数优化方法,并对工艺参数组合权重、利益比率进行试验验证。结果表明:当工艺参数优化组合为:油轮转速0.7 r/min,加工速度600 m/min,牵伸比1.55,油尺高度180 mm,第一热箱温度195 ℃,第二热箱温度150 ℃时,涤纶低弹丝的平均断裂强度提高15.84%,平均断裂伸长率提高4.04%,碳排放量降低4.58%,说明基于模糊多准则的工艺参数优化方法有利于解决工艺参数强耦合的问题。

关键词: 参数优化, 涤纶低弹丝, 模糊多准则, 碳排放量, 信噪比

Abstract:

The research reported in this paper aims to solve the problem of strong coupling among parameters in production of polyester drawn textured yarns.In parallel to the analysis of carbon emission in the production process of polyester drawn textured yarns, the strong coupling relationship among the process parameters was analyzed, leading to the selection of orthogonal test scheme based on the signal-to-noise ratio principle. On the basis of data preprocessing, the optimization method of process parameters based on fuzzy multi-criteria was constructed, and the combination weight and benefit ratio of process parameters were determined. The experimental results show that when the speed of oil tanker is 0.7 r/min, the processing speed is 600 m/min, the draft ratio is 1.55, the oil dipstick height is 180 mm, the temperature of the first hot box is 195 ℃and the temperature of the second hot box is 150 ℃,the average breaking strength of polyester drawn textured yarn is increased by 15.84%, the average elongation at break is increased by 4.04%, and the carbon emission is reduced by 4.58%, which fully show that the process parameter optimization method based on fuzzy multi-criteria workson the problem of strong coupling of process parameters.

Key words: parameter optimization, polyester drawn textured yarn, fuzzy multi-criteria, carbon emission, signaltonoise ratio

中图分类号: 

  • TQ340.69

图1

涤纶低弹丝生产过程碳排放分析模型"

表1

DTY加工参数及性能指标"

牵伸
油尺
高度/
mm
T1/℃ T2/℃ DTY
线密度/
dtex
断裂强度/
(cN·dtex-1)
断裂
伸长
率/%
沸水
收缩
率/%
1.50 200 175 145 87.8 3.72 22.0 2.8
1.50 215 185 150 88.4 3.75 21.8 3.2
1.55 220 195 165 88.8 3.62 22.0 2.7
1.60 220 195 165 87.8 3.70 20.7 3.0
1.55 180 195 155 86.9 3.72 23.0 3.1
1.55 240 205 160 86.2 3.71 19.7 3.2
1.65 240 215 165 86.9 3.75 22.4 2.6
1.50 190 195 155 87.1 3.68 20.5 3.5
1.50 200 185 150 87.7 3.69 22.0 3.0
1.55 220 195 155 90.3 3.73 23.2 2.9

表2

不同含油率下DTY的线密度及断裂强度"

含油率/% 线密度/dtex 断裂强度/(cN·dtex-1)
2.2 87 3.72
6.5 90 3.68

表3

加工不同线密度DTY的油轮转速及含油率"

线密度 油轮转速/(r·min-1) DTY含油率/%
83 dtex(72 f) 0.5 1.9
83 dtex(36 f) 0.6 2.2
111 dtex(36 f) 0.7 2.0
150 dtex(36 f) 0.8 2.9

表4

试验因素及水平"

水平 Vo/
(r·min-1)
Vl/
(m·min-1)
R Ho/mm T1/℃ T2/℃
1 0.4 500 1.50 180 175 145
2 0.5 550 1.55 200 185 150
3 0.6 600 1.60 220 195 155
4 0.7 650 1.65 240 205 160
5 0.8 700 1.70 260 215 165

表5

各试验方案指标数据计算后的信噪比值"

试验
序号
指标数据计算后的信噪比值
Ce Z1 Z2 Z3 Z4 Z5
1 -45.61 10.94 28.39 17.02 10.28 38.15
2 -45.68 10.85 28.33 17.48 10.03 38.18
3 -45.75 10.68 28.00 17.62 9.72 38.18
4 -45.82 10.32 27.36 16.92 9.82 38.12
5 -45.89 9.86 27.08 16.72 9.87 38.10
6 -45.82 10.63 28.22 18.93 9.14 38.30
7 -45.89 11.38 28.88 17.60 7.67 38.21
8 -45.71 11.07 27.16 15.39 11.85 38.02
9 -45.78 10.42 26.59 16.64 10.73 38.16
10 -45.86 10.39 27.85 19.12 9.72 38.32

表6

信噪比值规范化"

序号 Ce Z1 Z2 Z3 Z4 Z5
1 -0.201 136 0.202 888 0.205 114 0.193 077 0.205 474 0.199 757
2 -0.200 817 0.201 262 0.204 691 0.198 250 0.200 390 0.199 927
3 -0.200 506 0.198 017 0.202 306 0.199 900 0.194 237 0.199 932
4 -0.200 202 0.191 276 0.197 680 0.191 901 0.196 339 0.199 637
5 -0.199 898 0.182 906 0.195 611 0.189 691 0.197 177 0.199 502
6 -0.200 220 0.197 059 0.203 908 0.214 667 0.182 724 0.200 534
7 -0.199 908 0.211 088 0.208 668 0.199 626 0.153 273 0.200 060
8 -0.200 699 0.205 326 0.196 221 0.174 520 0.236 877 0.199 112
9 -0.200 396 0.193 158 0.192 108 0.188 717 0.214 356 0.199 809
10 -0.199 522 0.201 156 0.192 255 0.180 354 0.211 746 0.199 277

表7

涤纶低弹丝指标组合权重"

DTY指标 Ce Z1 Z2 Z3 Z4 Z5
组合权重 0.148 873 0.164 383 0.140 798 0.130 941 0.282 876 0.132 130

表8

每组试验的Si、Wi、Qi值"

试验序号 Si Wi Qi
1 0.502 541 0.148 873 0.380 203
2 0.484 318 0.125 979 0.287 203
3 0.517 337 0.144 272 0.386 116
4 0.608 795 0.137 162 0.486 681
5 0.665 814 0.164 383 0.644 377
6 0.446 360 0.183 227 0.408 422
7 0.474 648 0.282 876 0.745 196
8 0.520 743 0.132 130 0.354 215
9 0.594 027 0.140 798 0.477 908
10 0.426 197 0.143 956 0.263 687

表9

工艺参数平均Qi值"

因素 工艺参数水平
1 2 3 4 5 极差
V0 0.436 916 0.449 886 0.344 919 0.256 996 0.295 671 0.192 890
V1 0.332 804 0.378 599 0.302 849 0.436 516 0.333 619 0.133 668
R 0.308 505 0.285 003 0.377 018 0.388 915 0.424 946 0.139 943
H0 0.304 402 0.337 402 0.448 869 0.376 356 0.317 359 0.144 467
T1 0.365 217 0.319 292 0.277 598 0.291 302 0.530 978 0.253 380
T2 0.416 746 0.314 377 0.327 254 0.341 064 0.384 946 0.102 369

图2

关键工艺参数的残差分布图"

表10

工艺参数优化前后对比值"

类别 Vo /
(r·min-1)
V1/
(m·min-1)
R H0/
mm
T1/
T2/
优化前 0.4 700 1.70 220 205 160
优化后 0.7 600 1.55 180 195 150

表11

质量指标优化前后对比值"

类别 断裂强度/
(cN·dtex-1)
断裂伸
长率/%
卷曲收
缩率/%
沸水收
缩率/%
卷曲稳
定度/%
优化前 3.24 25.46 7.39 3.28 80.90
优化后 3.75 26.49 8.54 3.01 81.90
[1] MUTHU S S, LI Y, HU J Y, et al. Carbon footprint reduction in the textile process chain: recycling of textile materials[J]. Fibers and Polymers, 2012,13(8):1065-1070.
doi: 10.1007/s12221-012-1065-0
[2] VANDER VELDEN N M, PATEL M K, VOGTLANDER J G. LCA benchmarking study on textiles made of cotton, polyester, nylon, acryl, or elastane[J]. International Journal of Life Cycle Assessment, 2014,19(2):331-356.
doi: 10.1007/s11367-013-0626-9
[3] KHAYYAM H, NAEBE M, BABHADIASHAR A, et al. Stochastic optimization models for energy management in carbonization process of carbon fiberproduction[J]. Applied Energy, 2015,158(31):643-655.
doi: 10.1016/j.apenergy.2015.08.008
[4] RAILEANU S, ANTON F, IATAN A, et al. Resourcescheduling based on energy consumption for sustainable manufacturing[J]. Journal of Intelligent Manufacturing, 2017,28(7):1519-1530.
doi: 10.1007/s10845-015-1142-5
[5] KARTHIK T, MURUGAN R. Carbon footprint in denim manufacturing[M]. Sawston Cambridge: Woodhead Publishing, 2017: 125-159.
[6] FALLAHPOUR A R, MOGHASSEM A R. Spinning preparation parameters selection for rotor spun knitted fabric using VIKOR method of multicriteria decision-making[J]. Journal of The Textile Institute, 2013,104(1):7-17.
doi: 10.1080/00405000.2012.692939
[7] DEILAMANI M T, RASHIDI A, YAZDANSHENAS M E, et al. Effect of major false-twist texturing parameters on tensile properties and crystallinity of polyester microfilament yarn and optimized by RSM[J]. Bulgarian Chemical Communications, 2016,48:55-64.
[8] STOJANOVIC P, SAVIC M, TRAJKOVIC D, et al. The effect of false-twist texturing parameters on the structure and crimp properties of polyester yarn[J]. Chemical Industry & Chemical Engineering Quarterly, 2017,23(3):411-419.
[9] SONG R, CHEN S T. A self-tuning proportional- integral-derivative-based temperature control method for draw-texturing-yarn machine[J]. Mathematical Problems in Engineering, 2017,2017(11):1-17.
[10] 赵年花, 周翔, 董锋. 涤纶纺织品的碳足迹评估[J]. 印染, 2012,38(14):42-45.
ZHAO Nianhua, ZHOU Xiang, DONG Feng. Carbon footprint assessment of polyester textiles[J]. China Dyeing & Finishing, 2012,38(14):42-45.
[11] 王来力, 吴雄英, 丁雪梅, 等. 中国纺织服装行业能源消费碳排放因素分析[J]. 环境科学与技术, 2013,36(5):201-205.
WANG Laili, WU Xiongying, DING Xuemei, et al. Analysis of carbon emission for energy consumption in China's textiles and clothing sectors:based on LMDI Model[J]. Environmental Science & Technology, 2013,36(5):201-205.
[12] 刘笑莹, 方斌, 朱守艾, 等. 棉/大麻纤维混纺低损耗工艺优化[J]. 纺织学报, 2017,38(1):35-39.
LIU Xiaoying, FANG Bin, ZHU Shouai, et al. Low-loss optimization of cotton/hemp blending process[J]. Journal of Textile Research, 2017,38(1):35-39.
[13] 徐楠, 丁永生, 郝矿荣. 基于改进NSGA-Ⅱ的涤纶长丝熔体输送过程工艺优化[J]. 计算机与应用化学, 2012,29(7):825-828.
XU Nan, DING Yongsheng, HAO Kuangrong. Multi-objective optimization of polymer melt convey process in polyester fiber production using improved NSGA-Ⅱ[J]. Computers and Applied Chemistry, 2012,29(7):825-828.
[14] 刘琼, 田有全, SUTHERLAND J W, 等. 产品制造过程碳足迹核算及其优化问题[J]. 中国机械工程, 2015,26(17):2336-2343.
LIU Qiong, TIAN Youquan, SUTHERLAND J W, et al. Calculation and optimization of product carbon footprint in its manufacturing processes[J]. China Mechanical Engineering, 2015,26(17):2336-2343.
[15] 顾敏明, 戴文战. 涤纶织物热定型降耗优化算法[J]. 纺织学报, 2018,39(1):164-168.
GU Minming, DAI Wenzhan. Optimization algorithm for energy saving in heat setting of polyester fabric[J]. Journal of Textile Research, 2018,39(1):164-168.
[16] CHEN Y, WANG L C. A new calculation model based on multi-constraint relations oriented to carbon emission assessment of garment flexible manufacturing [C]//Proceedings of the 12th International on Fuzzy Logic and Intelligent Technologies in Nuclear Science Conference. France: World Scientific Publishing Co., Ltd., 2016: 656-661
[17] 俞璐, 王立川, 陈雁. 服装生产过程碳排放计算模型[J]. 纺织学报, 2016,37(3):156-159.
YU Lu, WANG Lichuan, CHEN Yan. Calculation model of carbon emission in garment production processes[J]. Journal of Textile Research, 2016,37(3):156-159.
[18] 邵景峰, 马创涛, 王蕊超, 等. 基于碳排放核算的涤纶低弹丝生产工艺优化[J]. 纺织学报, 2019,40(2):166-172.
SHAO Jingfeng, MA Chuangtao, WANG Ruichao, et al. Polyester drawn textured yarn production process optimizationbased on carbon emission accounting[J]. Journal of Textile Research, 2019,40(2):166-172.
[19] 邓朝晖, 符亚辉, 万林林, 等. 面向绿色高效制造的铣削工艺参数多目标优化[J]. 中国机械工程, 2017,28(19):2365-2372.
DENG Zhaohui, FU Yahui, WAN Linlin, et al. Multi objective optimization of milling process parameters for green high-performance manufacturing[J]. China Mechanical Engineering, 2017,28(19):2365-2372.
[20] 夏绪梅, 孙青青. 基于VIKOR法的地区专利成长性评价研究[J]. 科技管理研究, 2015,35(16):151-156.
XIA Xumei, SUN Qingqing. Research on evaluation of regional patent growth based on VIKOR method[J]. Science and Technology Management Research, 2015,35(16):151-156.
[21] 陈秀明, 刘业政. 基于熵权的多粒度犹豫模糊语言VIKOR群推荐方法[J]. 控制与决策, 2018,33(1):111-118.
CHEN Xiuming, LIU Yezheng. Multi-granular hesitant fuzzy linguistic term sets and their application in group recommendation based on entroy measure and VIKOR method[J]. Control and Decision, 2018,33(1):111-118.
[22] 傅为忠, 陈文静. 基于改进CRITIC-GGI-VIKOR的工业发展绿色度动态评价模型构建及其应用研究[J]. 科技管理研究, 2017,37(10):249-257.
FU Weizhong, CHEN Wenjing. Dynamic evaluation model building and application study of industrial development green degree based on improved CRITIC-GGI-VIKOR[J]. Science and Technology Management Research, 2017,37(10):249-257.
[23] 田会方, 舒服华. 基于VIKOR法的7050铝合金锻造工艺参数优化[J]. 特种铸造及有色合金, 2018,38(6):688-692.
TIAN Huifang, SHU Fuhua. Optimization of forging parameters for 7050 aluminum alloy based on VIKOR method[J]. Special Casting & Nonferrous Alloys, 2018,38(6):688-692.
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