Journal of Textile Research ›› 2022, Vol. 43 ›› Issue (11): 179-187.doi: 10.13475/j.fzxb.20210911709

• Machinery & Accessories • Previous Articles     Next Articles

Construction and experiment of intelligent chemicals distribution system for dyeing machine

ZHANG Fumu1, LIU Duanwu2, HU Yueming1()   

  1. 1. College of Automation Science and Engineering, South China University of Technology, Guangzhou, Guangdong 510640, China
    2. Foshan Nanhai Tianfu Technology Co., Ltd., Foshan, Guangdong 528222, China
  • Received:2021-09-29 Revised:2022-07-15 Online:2022-11-15 Published:2022-12-26
  • Contact: HU Yueming E-mail:auymhu@scut.edu.cn

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Abstract:

Aiming at the large distribution errors in traditional chemicals distribution systems for dyeing machines, a multi-layer fully connected neural network model was proposed based on the prediction of recommended pre-stop value and predicted time. The network model was firstly trained using data recorded in the distribution process, and the data to be distributed was fed into the trained network model for calculation so as to obtain the recommended pre-stop value and predicted time. The recommended pre-stop value and empirical pre-stop value were used to obtain the final pre-stop value according to the variable ratio algorithm. The system worked to determine the closing time of the distribution valve according to the final pre-stop value. The predicted time was used to evaluate whether the distribution process was timed out. Four pre-stop modes were used for chemicals distribution experiments over 1 000 times, and the results show that the standard deviation of distribution error predicted by network model is 23.8 g, the mean absolute error is 16.1 g. It is superior to the other three pre-stop modes with better chemical distribution accuracy.

Key words: chemicals distribution, pre-stop value, neural networks, variable radio, distribution accuracy, dyeing machine

CLC Number: 

  • TP23

Fig.1

Structure diagram of chemical distribution system of dyeing machine"

Fig.2

Relationship between flow rate and time when closing distribution valve"

Fig.3

Graph of delay output flow of flowmeter"

Fig.4

Relationship between chemicals ID and distribution error"

Fig.5

Relationship between tank ID and distribution error"

Fig.6

Relationship between volume and distribution error"

Fig.7

Fully connected neural network structure of pre-stop value and predict time"

Tab.1

Comparison of model training results"

数据集 预停值
损失值/g		 预停值平均
绝对误差/g		 预计用时
损失值/s		 预计用时
平均绝对
误差/s
训练集 564.10 15.23 2.24 1.02
校验集 588.86 15.40 2.11 1.00

Fig.8

Curves of pre-stop value and training epochs. (a) Loss curves of pre-stop value;(b) MAE curves of pre-stop value"

Fig.9

Curves of predict time and training epochs. (a) Loss curves of predict time value;(b) MAE curves of predict time value"

Fig.10

Graph of distribution error and distribution times of mode1 to mode 4"

Tab.2

Comparison of distribution error results of four modes"

模式 方差/g 标准差/g 平均绝对误差/g
模式1 1 022.0 32.0 158.4
模式2 907.4 30.1 61.4
模式3 746.1 27.3 32.8
模式4 564.1 23.8 16.1

Fig.11

Graph of actual time, predict time and distribution times"

Tab.3

Error statistics between actual and predict distribution time"

统计方法 方差/s 标准差/s 平均绝对误差/s
模式4(包含1次配送异常) 551.3 23.5 1.8
模式4(剔除1次配送异常) 2.5 1.6 1.0
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