纺织学报 ›› 2024, Vol. 45 ›› Issue (06): 173-185.doi: 10.13475/j.fzxb.20230402501

• 机械与设备 • 上一篇    下一篇

面向纺织印染的新一代数字制造系统

袁沐坤1,2,3, 于广平1,2, 刘坚2, 李健2, 王志广2, 苑明哲1,2, 郭清达1,2()   

  1. 1.中国科学院 沈阳自动化研究所, 辽宁 沈阳 110016
    2.广州工业智能研究院, 广东 广州 511458
    3.中国科学院大学, 北京 100049
  • 收稿日期:2023-04-18 修回日期:2024-01-17 出版日期:2024-06-15 发布日期:2024-06-15
  • 通讯作者: 郭清达(1981—),男,高级工程师,博士。主要研究方向为人工智能与工业互联网。E-mail: qingda0501@163.com
  • 作者简介:袁沐坤(1997—),男,博士生。主要研究方向为工业互联网、工业自动化。
  • 基金资助:
    中国博士后科学基金项目(2023M730779);广东省省级科技计划项目(2023A0505050090);羊城创新创业领军人才支持计划(2019006);“广东特支计划”杰出人才(2021JC06S567)

New generation of digital manufacturing system for textile printing and dyeing

YUAN Mukun1,2,3, YU Guangping1,2, LIU Jian2, LI Jian2, WANG Zhiguang2, YUAN Mingzhe1,2, GUO Qingda1,2()   

  1. 1. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
    2. Guangzhou Industrial Intelligence Research Institute, Guangzhou, Guangdong 511458, China
    3. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2023-04-18 Revised:2024-01-17 Published:2024-06-15 Online:2024-06-15

摘要:

针对纺织印染业数据感知、信息融合、知识推理、业务交互、人机协同等网络化、智能化产业升级需求,提出纺织印染新一代数字制造系统框架,结合业务生产全流程建立数据中台实现跨系统融合的综合智能决策;面向全生产决策领域构建决策支持组件池,推进业务系统与控制系统的融合;基于全业务生产流程数据的感知融合,搭建经营驾驶舱、订单数字化管控系统、设备数字化管控系统、能源数字化管控系统与环保安防数字化管控系统等,形成决策-执行闭环反馈的模式,实现业务和生产全流程信息管控。广东某纺织染整有限公司基于该纺织印染新一代数字制造系统框架对染色车间进行数字制造技术升级,与2019年度相比,该企业2021年度智能染色和定形示范车间人均产值提升145.0%和129.9%,良品率提升9.1%和2.6%,能源利用率提升31.9%和34.9%,在生产效率、产品质量、能源利用方面获得显著提升。

关键词: 纺织印染, 产业升级, 数字制造, 数据感知融合, 人机协同

Abstract:

Objective The textile industry, as a part of traditional and extensive manufacturing, features long and complex processes with specific requirements at each step. The widespread lack of informatization and automation results in low production efficiency and unstable quality control, demanding higher levels of process collaboration and control optimization. Additionally, being labor-intensive, it faces an increasing shortage of human resources. The industry's poor foundation in componentization and intelligence, alongside its weak flexibility and reconfiguration capabilities, faces pressures from resource scarcity, high energy consumption, and environmental costs, complicating the fulfillment of customization, diversity, small batches, and quality-focused transformation demands. The limitation of traditional digital manufacturing technologies on industry upgrades becomes apparent. The integration of the Industrial Internet, internet of things (IoT), big data, cloud computing, artificial intelligence (AI), and human-machine collaboration in new-generation digital manufacturing for textile printing and dyeing aims to meet demands for personalized customization, dynamic process reconstruction, and lean quality management. Textile production, spanning from negotiation to warehousing, encompasses various management areas like orders, design, and quality. Sharing data across these areas enables collaborative decision-making, supporting production and operational decisions through intelligent analysis and optimization, marking a key strategy in advancing collaborative manufacturing driven by digital information.

Method Addressing the digital transformation pain points in the textile printing and dyeing industry, such as decentralized cross-domain production data, weak interaction across management systems, and frequent manual involvement, the paper introduces a new-generation digital manufacturing system architecture for the textile and dyeing sector. It establishes a data hub for the business production process to collect and integrate data from various stages. By constructing decision-support components, the system enhances the customization capacity of the manufacturing system, meeting the optimization and control needs of different companies at various decision-making levels. Finally, it develops a comprehensive digital control system. Through creating a management cockpit, it achieves digital control over orders, equipment, energy, and environmental safety, fulfilling the industry's needs for integrated intelligent decision-making across systems, merging business and control systems, and a closed-loop control model from decision to execution.

Results Paper proposes a new-generation digital manufacturing system framework for textile printing and dyeing to meet the needs of networked and intelligent industrial upgrading such as data perception, information fusion, knowledge reasoning, business interaction, and human-computer collaboration in there. It combines the whole process of business production to establish data center to achieve cross-system integration of integrated intelligent decision-making, build a pool of decision support components for the whole production decision-making field, which is used to promote the integration of business systems and control systems, and set up management cockpit, order digital control system, equipment digital control system, energy digital control system and environmental protection and security digital control system based on the integration of the whole business production process data. Finally, the model of decision-execution closed-loop feedback is formed to realize the information control of the whole process of business and production. A textile dyeing and finishing company in Guangdong, based on the system framework, upgraded the digital manufacturing technology in its dyeing workshop. Compared to 2019, in 2021, the digital intelligent dyeing demonstration workshop saw its per capita output value increase from 1.140 4 million yuan per person to 2.791 1 million yuan per person, with production efficiency up by 145.0%. The product yield rate rose from 88.2% to 96.2%, marking a 9.1% quality improvement. Energy consumption per unit of output value decreased from 465.12 kgce per ten thousand yuan to 316.71 kgce per ten thousand yuan, enhancing energy utilization by 31.9%. In the digital intelligent shaping demonstration workshop, the per capita output value grew from 0.455 2 million yuan per person to 1.046 6 million yuan per person, a 129.9% increase in production efficiency. The product yield rate improved from 95.1% to 97.6%, a 2.6% quality increase, and energy consumption per unit of output value was reduced from 1 759.83 kgce per ten thousand yuan to 1 144.87 kgce per ten thousand yuan, resulting in a 34.9% increase in energy utilization. Significant improvements were achieved in terms of production efficiency, product quality, and energy utilization.

Conclusion Textile printing and dyeing industry already gets a basic digital foundation and information level, with the process of deploying advanced computer numerical control equipment with functions such as equipment communication, status monitoring, process monitoring and fault diagnosis in weaving, dyeing, sizing, color matching, auxiliaries' transmission and distribution and dyestuff transmission and distribution. The popularity of software systems such as enterprise resource planning (ERP), manufacturing execution system (MES) and scheduling system (APS) applied in enterprise procurement, production, marketing and management can provide a timely channel to understand the operation status of workshop production, and significantly reduce the defective rate of products and improve the production operation level of enterprises. The future development and innovation direction of the textile printing and dyeing industry is based on the needs of production enterprises to increase efficiency, reduce consumption, and optimize management. By perceiving production data to form industry domain knowledge, creating a common operation and management platform, and strengthening the application and promotion of this platform in upstream and downstream enterprises, it aims to integrate the supply chain, production chain, and sales chain, forming a collaborative operation and management system for the entire value chain.

Key words: textile printing and dyeing, industrial upgrading, digital manufacturing, full-service production process, human-machine collaboration

中图分类号: 

  • TS108.8

图1

订单跟踪优化环节的信息孤岛"

图2

纺织印染行业跨层跨域数据感知融合框架"

图3

纺织印染行业人机协同决策流程"

图4

纺织印染数字制造系统结构"

图5

跨层跨域数据感知融合流程"

表1

数据采集方法"

数据源形式 设备 数据采集难点 解决方法
接口开放型 PLC控制系统、数字化检测仪表等 数据接口与通讯协议繁杂不统一 OPC数据采集软件:对接现场工控机,读取数据与本地/云端服务器进行通信采集
工业物联网智能网关:对接现场设备支持Modbus RTU/Modbus TCP、主流可编程逻辑控制器(PLC)协议等多类型通讯协议,按需主动精准采集现场数据
第三方智能网关:对接第三方智能网关,支持MQTT、HTTP/HTTPS等网络协议接入云端服务器
接口封闭型 进口装备、高度集成装备等 数据接口封闭无法在线采集 图像识别智能网关:获取工控机协同界面图像,在线按需精准识别和转化图像数据
无数据接口型 老式数显流量计、温度计、压力计等 没有数据协议只可现场抄数 图像识别智能网关:捕捉设备界面并形成图像,在线按需精准识别和转化图像数据

图6

纺织印染领域知识自主获取流程"

图7

业务生产流程信息管控流程"

图8

决策支持组件与数字管控系统"

图9

染色车间内部分设备及数控系统"

表2

数字化制造技术升级成效指标"

车间 指标 2019年 2021年 对比
数字化智
能染色示
范车间
产值/万元 77** 13*** 72.4% (↑)
良品率/% 88.2 96.2 9.1% (↑)
产量/t 96** 11*** 21.3% (↑)
总使用标煤/t 36** 43** 20.1% (↑)
数字化智
能定形示
范车间
产值/万元 33** 58** 72.4% (↑)
良品率/% 95.1 97.6 9.1% (↑)
产量/t 10*** 12*** 14.6% (↑)
总使用标煤/t 58** 67** 20.1% (↑)
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