纺织学报 ›› 2023, Vol. 44 ›› Issue (05): 191-197.doi: 10.13475/j.fzxb.20220405201

• 服装工程 • 上一篇    下一篇

基于仿真区域性数据的服装团体定制归号机制

聂梓萌1, 杜劲松1,2(), 朱建龙3, 岳春明3, 葛旭光4   

  1. 1.东华大学 服装与艺术设计学院, 上海 200051
    2.东华大学 现代服装设计与技术教育部重点实验室, 上海 200051
    3.海澜之家集团股份有限公司, 江苏 江阴 214426
    4.福建省希领服饰有限责任公司, 福建 泉州 362201
  • 收稿日期:2022-04-14 修回日期:2022-12-26 出版日期:2023-05-15 发布日期:2023-06-09
  • 通讯作者: 杜劲松(1970—),男,副教授,博士。主要研究方向为服装智能制造。E-mail:ducccp@dhu.edu.cn。
  • 作者简介:聂梓萌(1998—),女,硕士生。主要研究方向为服装工程数字化。
  • 基金资助:
    中央高校基本科研业务费专项资金资助项目(2232020G)

Garment group customization sizing mechanism based on simulated size data

NIE Zimeng1, DU Jinsong1,2(), ZHU Jianlong3, YUE Chunming3, GE Xuguang4   

  1. 1. College of Fashion and Design, Donghua University, Shanghai 200051, China
    2. Key Laboratory of Clothing Design and Technology, Ministry of Education, Donghua University, Shanghai 200051, China
    3. Hta Group Co., Ltd., Jiangyin, Jiangsu 214426, China
    4. Fujian Xiling Clothing Co., Ltd., Quanzhou, Fujian 362201, China
  • Received:2022-04-14 Revised:2022-12-26 Published:2023-05-15 Online:2023-06-09

RichHTML

9

PDF (PC)

46

摘要：

在服装团体定制市场快速发展的背景下,为探究适应不同团体客户的定制方案,解决区域性大规模人体尺寸数据收集难的问题,优化团体定制归号流程和提升团体订单风格统一性,采用蒙特卡罗方法仿真区域性团体尺寸测量数据,建立区域性团体号型规格表(RGS),并提出基于比例方法归号流程,优化归号结构链。结果表明:仿真数据与初始订单数据的散点图分布一致,仿真尺寸数据均呈正态分布,达到预期仿真目标。分别采用企业初始号型规格表(EIS)和区域性团体号型规格表对订单尺寸进行归号,RGS归号结果评价等级优秀转化为47.4%,表明优化的归号流程有效,且RGS更贴合目标区域人群体型,号型覆盖评价结果显示RGS对区域人群的整体覆盖效果显著。

关键词: 服装团体定制, 数据仿真, K-means聚类算法, 蒙特卡罗方法, 服装归号

Abstract:

Objective In the context of the rapid development of group customization of garment, this paper aims to solve the current problem of regional size system establishment of group customization, optimize the group customization number sizing process, and improve the style unity of group orders.

Method The Monte Carlo method was adopted to simulate the regional group size measurement data, and the regional group size system (RGS) was established based on the K-means clustering analysis results of the simulation data and the size proportion coefficient of the enterprise initial size system (EIS) and the sizing process of the group order was realized by using RGS. The sizing results were evaluated by comprehensive fit and aggregate loss.

Results The scatter diagram of chest grith/garment length of 10 000 sets of data was simulated by Monte Carlo method. The scatter diagram distribution and frequency distribution statistics of simulation data and real data were consistent (Fig.4), suggesting that the simulated data from the simulation model could be used as the target data of the next experiment. The clustering center of the simulation data clustering analysis results was tested (Tab.2). Based on the clustering center and EIS, the regional group size system RGS was established through the size system establishment process. 19 Types of RGS were involved in the research (Tab.4). Each size contained 4 important primary dimensions, i.e. chest grith, garment length, across shoulder and sleeve length. It was found that 4 sizes determining the style of garment were mid waist, hipline, sleeve bicep, and cuff. The same circumference or width transverse parameter has 3 length index values to match, and there were more size combination. Compared with EIS, RGS established on the basis of simulation data demonstrated a significant effect on the overall coverage of regional population. The comprehensive fit results showed that RGS can cover most target groups (Fig.5). The size data of the orders containing 218 people of the enterprise were matched by EIS and RGS, respectively. The sizing process and the sizing results were suggested 1 person cut individually for special body type. When using EIS for matching sizes, the evaluation result of the fit degree was as follows: 108 people fedback with excellent fitting (48%), 102 people good, 1 person general, and 6 people not ideal. When RGS was adopted to match the size of the order, and the fit degree evaluation results showed that 211 people returned excellent feedback, 4 people good, and 2 people general, with the proportion of excellent of 97.2%. The excellent conversion rate of RGS is 47.4% (Tab.5).

Conclusion The Monte Carlo method was adopted to simulate the regional size data accumulated by enterprise orders, and the enterprise size database was established. The simulation data reached the expected simulation goal, and the regional group size system could be optimized by using the simulation data. RGS establishment is shown to increase the coverage of target population, increase the fit degree of clothing, effectively improve the uniformity and consistency of garment customization, and thus reduce the probability of garment repair. The sizing mechanism considers the multidimensional size proportion of human body, changes the method of relying on a single dimension for the sizing process, and carries on the size matching through the proportion of different dimensions. The sizing process can effectively distinguish and classify the body size, and match the individual size with the size system. The sizing mechanism can provide theoretical basis for enterprise digital sizing process.

Key words: garment group customization, data simulation, K-means clustering algorithm, Monte Carlo method, garment sizing process

中图分类号: 

  • TS941

表1

不同定制模式对比分析"

定制模式 应用模式 尺寸测量方案 号型标准制定 纸样生成方式 号型标准应用 版型变体方法 裁剪方案
大规模
定制生产		 C2M 控制尺寸、特征尺寸 国家标准、企业标准 企业版型、独立生成 服装通用号型标准 单独生成、批量修正 单件剪裁、粗裁+精裁
个性化定制 高级定制、C2M 控制尺寸、特征尺寸、其他尺寸 无标准、企业标准 独立生成 无 单独生成 单件剪裁
团体定制 制服、校服、团订 控制尺寸、特征尺寸 国家标准、企业标准、区域标准 企业版型,批量生成 区域性号型标准 批量修正 单件剪裁、粗裁+精裁

图1

归号流程"

图2

仿真区域性人体数据流程"

图3

团订归号流程机制模型"

图4

真实数据与仿真数据统计分布比较"

表2

聚类中心数据"

聚类中心序号 胸围 C p 1 衣长 C p 2 肩宽 C p 3 袖长 C p 4
1 95.51 69.79 42.77 57.86
2 97.97 73.37 43.81 61.04
3 100.08 70.27 44.10 57.92
4 101.44 73.13 44.72 60.19
5 102.59 75.83 45.27 62.58
6 104.03 71.19 45.19 58.23
7 104.44 73.64 45.57 60.50
8 106.42 77.88 46.45 63.82
9 106.49 75.25 46.24 61.43
10 107.18 72.53 46.17 58.95
11 109.28 76.34 47.13 62.33
12 109.81 74.26 47.04 60.06
13 110.85 78.95 47.81 64.24
14 112.65 76.14 48.02 61.40
15 115.22 78.74 48.94 63.52

表3

HL企业Y版基础号型规格表"

号型 胸围
E p 1		 衣长
E p 2		 肩宽
E p 3		 袖长
E p 4		 中腰围
E s 1		 臀围
E s 2		 袖肥
E s 3		 袖口
E s 4
90/71 90 71 40.8 56.5 79 90 17.4 12.5
92/71 92 71 41.4 56.5 81 92 17.7 12.7
94/71 94 71 42.0 56.5 83 94 18.0 12.9
96/71 96 71 42.6 56.5 85 96 18.3 13.1
98/71 98 71 43.2 56.5 87 98 18.6 13.3
100/73 100 73 43.8 58.0 89 100 18.9 13.5
102/73 102 73 44.4 58.0 91 102 19.2 13.7
104/73 104 73 45.0 58.0 93 104 19.5 13.9
106/75 106 75 45.6 59.5 95 106 19.8 14.1
108/75 108 75 46.2 59.5 97 108 20.1 14.3
110/75 110 75 46.8 59.5 99 110 20.4 14.5
112/75 112 75 47.4 59.5 101 112 20.7 14.7
114/77 114 77 48.0 61.0 103 114 21.0 14.9
116/77 116 77 48.6 61.0 105 116 21.3 15.1
118/77 118 77 49.2 61.0 107 118 21.6 15.3

表4

Y版区域号型规格表"

RGS号型序号 胸围 R p 1 衣长 R p 2 肩宽 R p 3 袖长 R p 4 中腰围 R s 1 臀围 R s 2 袖肥 R s 3 袖口 R s 4 归号结果
1 91.0 69/71/73 40.9 57/58/59 80.0 91.0 17.6 12.6 1
2 92.5 69/71/73 41.4 57/58/59 81.5 92.5 17.8 12.8 0
3 94.0 69/71/73 41.8 57/58/59 83.0 94.0 18.0 12.9 0
4 95.5 71/73/75 42.3 58/59/60 84.5 95.5 18.2 13.1 0
5 97.0 71/73/75 427 58/59/60 86.0 97.0 18.5 13.2 3
6 98.5 71/73/75 43.2 58/59/60 87.5 98.5 18.7 13.4 9
7 100.0 71/73/75 43.6 58/59/60 89.0 100.0 18.9 13.5 10
8 101.5 71/73/75 44.1 58/59/60 90.5 101.5 19.1 13.7 0
9 103.0 71/73/75/77 44.5 58/59/60/61 92.0 103.0 19.4 13.8 33
10 104.5 71/73/75/77 45.0 58/59/60/61 93.5 104.5 19.6 14.0 31
11 106.0 7173/75/77 45.4 58/59/60/61 95.0 106.0 19.8 14.1 31
12 107.5 71/73/75/77 45.9 58/59/60/61 96.5 107.5 20.0 14.3 0
13 109.0 71/73/75/77 46.3 58/59/60/61 98.0 109.0 20.3 14.4 40
14 110.5 75/77/79 46.8 60/61/62 99.5 110.5 20.5 14.6 19
15 112.0 75/77/79 47.2 60/61/62 101.0 112.0 20.7 14.7 20
16 113.5 75/77/79 47.7 60/61/62 102.5 113.5 20.9 14.9 0
17 115.0 75/77/79 48.1 60/61/62 104.0 115.0 21.2 15.0 10
18 116.5 75/77/79 48.6 60/61/62 105.5 116.5 21.4 15.2 7
19 118.0 75/77/79 49.0 60/61/62 107.0 118.0 21.6 15.3 3

表5

归号结果评价等级表"

合体等级评价
范围/cm		 评价等级 统一性 EIS
(优化前)		 RGS
(优化后)
0~1 一级 优秀 108 211
1~2 二级 良好 102 4
2~3 三级 一般 1 2
3~4 四级 不理想 6 0

图5

号型覆盖率比较分析"

[1] 邓椿山, 李琴, 周莉, 等. 体型分析在观测服装号型适应性上的应用[J]. 纺织学报, 2017, 38(1):111-115.
DENG Chunshan, LI Qin, ZHOU Li, et al. Application of body shape analysis in observation of clothing size adaptability[J]. Journal of Textile Research, 2017, 38(1):111-115.
[2] XIA S B, ISTOOK C. A method to create body sizing systems[J]. Clothing and Textiles Research Journal, 2017, 35(4): 235-248.
doi: 10.1177/0887302X17713298
[3] 余佳佳, 李健. 中国东部地区青年女性人体体型分类[J]. 纺织学报, 2020, 41(5):134-139.
YU Jiajia, LI Jian. Classification of young women's somatotypes in Eastern China[J]. Journal of Textile Research, 2020, 41(5):134-139.
doi: 10.1177/004051757104100208
[4] XU Y, THOMASSEY S, ZENG X. Garment mass customization methods for the cutting-related processes[J]. Textile Research Journal, 2021, 91(7/8): 802-819.
doi: 10.1177/0040517520957399
[5] 文德智, 卓仁鸿, 丁大杰, 等. 蒙特卡罗模拟中相关变量随机数序列的产生方法[J]. 物理学报, 2012, 61(22):26-33.
WEN Zhide, ZHUO Renhong, DING Dajie, et al. Generation of correlated pseudorandom variables in Monte Carlo simulation[J]. Acta Physica Sinica, 2012, 61(22):26-33.
[6] 胡娟. 基于仿真人体大数据的高粒度服装号型重构及实践[D]. 北京: 北京服装学院, 2019:22-24.
HU Juan. A high-granularity sizing system for apparel customization based on large-scale simulated data of human body measurements[D]. Beijing: Beijing Institute of Fashion Technology, 2019:22-24.
[7] 王建萍, 李月丽, 喻芳. 基于择近原则的服装号型数字化归档方法[J]. 纺织学报, 2007, 28(11):106-110.
WANG Jianping, LI Yueli, YU Fang. Digitized categorization for garment size designation based on the approaching principle[J]. Journal of Textile Research, 2007, 28(11):106-110.
[8] 张恒, 张欣. 基于批量定制的服装号型归档方法[J]. 东华大学学报(自然科学版), 2009, 35(4):436-440.
ZHANG Heng, ZHANG Xin. The type filing for garment size type based on the lot custom-made[J]. Journal of Donghua University (Natural Science), 2009, 35(4):436-440.
[9] 呼慧敏, 晁储芝, 赵朝义, 等. 中国成年人人体尺寸数据相关性研究[J]. 人类工效学, 2014, 20(3):49-53.
HU Huimin, CHAO Chuzhi, ZHAO Chaoyi, et al. A study on the correlation of human body size data among Chinese adults[J]. Chinese Journal of Ergonomics, 2014, 20(3):49-53.
[10] XU Y, THOMASSEY S, ZENG X. Optimization of garment sizing and cutting order planning in the context of mass customization[J]. International Journal of Advanced Manufacturing Technology, 2020, 106(7/8): 3485-3503.
doi: 10.1007/s00170-019-04866-w
[1] 任艳博, 蒋超, 王教庆, 俞琳, 王园园. 基于聚类算法和色彩网络的蝴蝶色彩分析及应用[J]. 纺织学报, 2021, 42(05): 103-108.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备14006648号　 京公网安备11010502044800号
版权所有 © 2021 《纺织学报》编辑部
地址： 北京市朝阳区延静里中街3号主楼6层《纺织学报》杂志社　邮政编码：100025　
电话：010-65017711,65917740　传真：010-65016539　Email:fangzhixuebao@vip.126.com
本系统由北京玛格泰克科技发展有限公司设计开发