Journal of Textile Research ›› 2020, Vol. 41 ›› Issue (11): 162-167.doi: 10.13475/j.fzxb.20190905606

• Machinery & Accessories • Previous Articles     Next Articles

Automatic generation algorithm for pattern processing codes of quilting machines

LI Liang, NI Junfang()   

  1. School of Mechanical and Electrical Engineering, Soochow University, Suzhou, Jiangsu 215021, China
  • Received:2019-09-24 Revised:2020-08-07 Online:2020-11-15 Published:2020-11-26
  • Contact: NI Junfang E-mail:jfni9999@sina.com

RichHTML

3

PDF (PC)

178

Abstract:

In order to simplify the pattern design process with the quilting machines, to reduce the influence of operators' experience and to achieve the effect of the real-time quilting, this research worked on the automatic generation of pattern codes. The target quilting pattern is pre-processed and edge-extracted, and the contour tracking algorithm was used to segment the image into several ordered point-chain contours. The genetic algorithm was used with the line and arc as base processing elements and the first-order continuous continuity as constraint condition, to fit each outline contour to carry out simulation machining. Programming verification was carried out to prove the reliability of the result by MatLab. The results show that the constraint error function is able to avoid the influence of the trimming and stitch skipping caused by the discontinuity of the endpoint. The fitness of optimal processing path of each generation is gradually improved by the action of the genetic operators. The total error and the number of fitting segments are smaller. The pattern simulation processing satisfies the requirements of good real-time performance, high efficiency, high degree of reduction and the needs of quilting manufacturing requirements of complex patterns in practice.

Key words: quilting machine, pattern generation algorithm, contour tracking, genetic algorithm, curve fitting, simulation processing

CLC Number: 

  • TS391

Fig.1

Image preprocessing"

Fig.2

Singular points"

Fig.3

Example of contour tracking algorithm. (a) Test bitmap image;(b) Segmentation results"

Fig.4

Flow chart of contour tracking algorithm"

Fig.5

Base processing element fitting result"

Fig.6

Optimal processing path"

Fig.7

Optimal fitness of each generation"

Fig.8

Quilting machine simulation processing"

[1] 赵福英, 倪俊芳. 花型样条曲线加工代码生成算法[J]. 纺织学报, 2018,39(7):153-158.
ZHAO Fuying, NI Junfang. Algorithm of processing code genereation for pattern spline curves[J]. Journal of Textile Research, 2018,39(7):153-158.
[2] 邢毓华, 巩少峰. 电子花样机花样缝纫点生成算法[J]. 计算机系统应用, 2018,27(6):171-177.
XING Yuhua, GONG Shaofeng. Pattern sewing point generation algorithm for intelligent pattern sewing machine[J]. Computer Systems Applications, 2018,27(6):171-177.
[3] 张伟宁. 基于机器视觉的描绣花样矢量化技术的研究[D]. 北京:北京交通大学, 2015: 46-49.
ZHANG Weining. Research on the vector technique of embroidery pattern based on machine vision[D]. Beijing: Beijing Jiaotong University, 2015: 46-49.
[4] 史步海, 孙会会. 基于新S型速度规划的B样条曲线算法研究[J]. 机床与液压, 2016,44(15):72-79.
SHI Buhai, SUN Huihui. B-spline curve algorithm research based on news velocity planning[J]. Machine Tool & Hydraulics, 2016,44(15):72-79.
[5] DING Huanqi, LUO Shanming, CHANG Xuefeng, et al. Optimization algorithm of tool radius compensation in fast tool servo machining of microlens array[J]. Advanced Materials Reseach, 2014,1039:383-389.
[6] 孙喜庆. 小线段轨迹的圆弧拟合和NURBS曲线的离散算法研究[D]. 哈尔滨:哈尔滨工业大学, 2015: 7-12.
SUN Xiqing. The research of the continuous line segments fitting by arc and the separation of NURBS curve[D]. Harbin:Harbin Institute of Technology, 2015: 7-12.
[7] 倪俊芳, 宋昌才, 何高清. 机床数控技术[M]. 北京: 科学出版社, 2016: 34-50.
NI Junfang, SONG Changcai, HE Gaoqing. Machine tool numerical control technology [M]. Beijing: Science Press, 2016: 34-50.
[8] 胡晋山, 康建荣, 张琪, 等. 一种八邻域图像边界追踪改进算法[J]. 测绘通报, 2018(12):21-25.
HU Jinshan, TANG Jianrong, ZHANG Qi, et al. An improving image boundary tracking algorithm based on eight neighborhood[J]. Bulletin of Surveying and Mapping, 2018(12):21-25.
[9] 姚鹏鹏, 樊臻, 张森林. 改进的Potrace提花织物图像矢量化算法[J]. 传感器与微系统, 2014,33(4):125-127.
YAO Pengpeng, FAN Zhen, ZHANG Senlin. Improved potrace algorithm for vectorizaition of image of jacquard[J]. Transducer and Microsystem Technologies, 2014,33(4):125-127.
[10] ZHANG Weichuan, KONG Xiangnan, SONG Wen. Review of image corner detection algorithms[J]. Acta Electronica Sinica, 2015,43(11):2315-2321.
doi: 10.3969/j.issn.0372-2112.2015.11.026
[11] 张习文, 李佐, 蔡士杰, 等. 基于遗传算法的以线段和圆弧为基元的曲线拟合[J]. 计算机辅助设计与图形学学报, 2002(2):144-147.
ZHANG Xiwen, LI Zuo, CAI Shijie, et al. Segmenting planar curves into straight line and circular arcs segments using genetic algorithm[J]. Journal of Computer-Aided Design & Computer Graphics, 2002(2):144-147.
[12] 王竞雪, 宋伟东, 赵丽科, 等. 改进的Freeman链码在边缘跟踪及直线提取中的应用研究[J]. 信号处理, 2014,30(4):422-430.
WANG Jingxue, SONG Weidong, ZHAO Like, et al. Application of improved freeman chain code in edge tracking and straight line extraction[J]. Journal of Signal Processing, 2014,30(4):422-430.
[1] ZHANG Zhuo, CONG Honglian, JIANG Gaoming, DONG Zhijia. Polo shirt rapid style recommendation system based on interactive genetic algorithm [J]. Journal of Textile Research, 2021, 42(01): 138-144.
[2] XIE Ziang, DU Jinsong, ZHAO Guohua. Adaptive dynamic scheduling of garment hanging production line [J]. Journal of Textile Research, 2020, 41(10): 144-149.
[3] ZHANG Xiaoxia, LIU Fengkun, MAI Wei, MA Chongqi. Prediction of loom efficiency based on BP neural network and its improved algorithm [J]. Journal of Textile Research, 2020, 41(08): 121-127.
[4] YING Shuangshuang, QIU Kebin, GUO Yufei, ZHOU Jiu, ZHOU Hua. Error optimization for measuring color chart data in textile color management [J]. Journal of Textile Research, 2020, 41(08): 74-80.
[5] HUANG Zhenzhen, MOK Pikyin, WEN Lihong. Garment production line balance based on genetic algorithm and simulation [J]. Journal of Textile Research, 2020, 41(07): 154-159.
[6] ZHENG Xiaohu, BAO Jinsong, MA Qingwen, ZHOU Heng, ZHANG Liangshan. Spinning workshop collaborative scheduling method based on simulated annealing genetic algorithm [J]. Journal of Textile Research, 2020, 41(06): 36-41.
[7] MO Shuai, FENG Zhanyong, TANG Wenjie, DANG Heyu, ZOU Zhenxing. Performance optimization of elastic spindle pipe based on neural network and genetic algorithm [J]. Journal of Textile Research, 2020, 41(04): 161-166.
[8] HUANG Qi, ZHOU Qihong, ZHANG Qian, WANG Shaozong, FAN Wei, SUN Huifeng. Layout optimization of dip dyeing workshop based on system layout planning-genetic algorithm [J]. Journal of Textile Research, 2020, 41(03): 84-90.
[9] ZHANG Xujing, WANG Lichuan, CHEN Yan. Balancing optimization of garment sewing assembly line based on genetic algorithm [J]. Journal of Textile Research, 2020, 41(02): 125-129.
[10] WANG Xiaohui, LIU Yuegang, MENG Zhuo, SUN Yize. Optimization of process parameters for 3D additive screen printing based on genetic algorithm and neural network [J]. Journal of Textile Research, 2019, 40(11): 168-174.
[11] MENG Shuo, PAN Ruru, GAO Weidong, WANG Jing'an, ZHOU Lijun. Research on weaving scheduling using main objective evolutionary genetic algorithm [J]. Journal of Textile Research, 2019, 40(08): 169-174.
[12] . Optimum and application of automatic cotton blending based on high volume instrument data by improved genetic algorithm [J]. JOURNAL OF TEXTILE RESEARCH, 2016, 37(09): 151-155.
[13] . Worsted spinning process parameters inversion model using mixed population genetic neural network [J]. JOURNAL OF TEXTILE RESEARCH, 2016, 37(07): 149-154.
[14] . Construction of radial basis function neural network models for typical cross section curve of shorts [J]. JOURNAL OF TEXTILE RESEARCH, 2015, 36(05): 83-88.
[15] . Grade evaluation of color fastness to laundering based on image analysis [J]. JOURNAL OF TEXTILE RESEARCH, 2013, 34(11): 100-0.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备14006648号
Copyright 2021 © Editorial office of Journal of Textile Research
Supported by Beijing Magtech Co.Ltd