人工智能在纺织印染行业中的应用研究进展

doi:10.13475/j.fzxb.20250404402

纺织学报 ›› 2025, Vol. 46 ›› Issue (12): 260-269.doi: 10.13475/j.fzxb.20250404402

人工智能在纺织印染行业中的应用研究进展

周青青¹^,²^,³, 常硕¹, 毛志平²^,³, 吴伟²()

1.嘉兴大学材料与纺织工程学院, 浙江嘉兴 314001
2.东华大学化学与化工学院, 上海 201620
3.山东中康国创先进印染技术研究院有限公司, 山东泰安 271000

收稿日期:2025-04-28 修回日期:2025-09-12 出版日期:2025-12-15 发布日期:2026-02-06
通讯作者: 吴伟(1991—),男,讲师,博士。主要研究方向为计算化学和人工智能方法在新型染化料分子结构设计中的应用。E-mail:wuwei@dhu.edu.cn。
作者简介:周青青(1990—),女,讲师,博士。主要研究方向为低碳可持续印染加工技术。
基金资助:
嘉兴市青年科技人才专项项目(2023AY40027);国家自然科学基金项目(22208049)

Research progress in applications of artificial intelligence in dyeing and finishing industry

ZHOU Qingqing¹^,²^,³, CHANG Shuo¹, MAO Zhiping²^,³, WU Wei²()

1. College of Materials Science and Textile Engineering, Jiaxing University, Jiaxing, Zhejiang 314001, China
2. College of Chemistry and Chemical Engineering, Donghua University, Shanghai 201620, China
3. Shandong Zhongkang Guochuang Advanced Dyeing and Finishing Technology Research Institute Co., Ltd., Tai'an, Shandong 271000, China

Received:2025-04-28 Revised:2025-09-12 Published:2025-12-15 Online:2026-02-06

摘要/Abstract

摘要：

针对人工智能(AI)在纺织印染行业应用日益广泛但缺乏系统性梳理的现状,通过系统性文献调研与分析,归纳了机器学习(ML)、深度学习(DL)等AI技术在染料性质预测与结构设计、智能测配色、工艺优化与生产管理及质量检测四大核心领域的应用进展与成效。研究表明:在染料开发中,AI通过性质预测和高通量虚拟筛选,显著加速了研发进程;在测配色方面,基于ML/DL的模型提升了配方预测精度,并结合高光谱成像等技术克服了同色异谱等难题;在工艺优化上,AI实现了上染率、K/S值等关键指标的有效预测,并应用于生产调度与异常监控;在质量检测领域,基于无监督学习的图像重建技术有效提升了疵点检出率,结合光谱技术实现了高效的“免染色”质量预评估。总体而言,AI正推动行业向智能化、绿色化转型,但仍面临数据稀缺、模型泛化能力和系统集成等挑战,未来需聚焦于构建贯穿全流程的智能制造体系。

关键词: 印染, 人工智能, 染料设计, 测配色, 工艺优化, 质量检测, 机器学习, 深度学习

Abstract:

Significance Dyeing and finishing industry, a cornerstone of traditional textile manufacturing, is at a critical juncture. It faces a confluence of modern challenges, including rising consumer demand for personalized products, increasingly stringent environmental regulations, and intense global market competition. The industry's conventional production models, which rely heavily on empirical experience, are proving inadequate, often leading to low efficiency, high consumption of resources, and inconsistent product quality. Artificial Intelligence (AI) has emerged as a key enabling technology to navigate these complexities. By leveraging its powerful capabilities in data processing, pattern recognition, and predictive optimization, AI offers a transformative pathway for the industry to achieve intelligent, green, and highly efficient manufacturing. This review provides a systematic evaluation of recent progress in AI applications across the textile dyeing and finishing landscape, aiming to clarify the current state of AI adoption, identify key challenges, and chart a course for its future trajectory in the industry.

Progress AI is being integrated into every stage of the textile dyeing process, yielding significant advancements. In dye design, AI revolutionizes development by accurately predicting crucial properties (e.g. maximum absorption wavelength(λ_max), solubility) pre-synthesis and facilitating high-throughput virtual screening, dramatically accelerating the discovery of novel dyes. For color management, AI overcomes the limitations of traditional methods. It learns the complex relationship between color attributes and dye recipes from data, solves the "one-to-many" recipe problem, and mitigates metamerism by integrating with hyperspectral imaging. In process optimization, AI introduces a new level of precision. It predicts key dyeing outcomes (e.g., K/S value, exhaustion rates), allowing for the optimization of dyeing conditions to minimize resource consumption and maximize quality. It also solves complex production scheduling problems and enables real-time anomaly monitoring. For quality inspection, AI is driving the shift towards full automation. Unsupervised, reconstruction-based methods have become the dominant solution for defect detection, effectively addressing the challenge of scarce defect samples. A forward-looking application is "dyeing-free" quality prediction, where spectroscopy and ML are used to analyze raw materials to predict their final dyeing uniformity.

Conclusion and Prospect AI technologies are fundamentally reshaping the textile dyeing and finishing industry, demonstrating immense potential to enhance innovation, efficiency, and sustainability. Research has led to clear achievements in accelerating dye discovery, improving color recipe accuracy, optimizing resource utilization, and automating quality control. However, widespread adoption of AI technology faces significant hurdles. These include a critical lack of high-quality, standardized industry data, challenges in ensuring model robustness and generalization from lab environments to real-world production fluctuations, and the high technical and financial costs of integrating AI systems into the existing manufacturing infrastructure. Future research must focus on overcoming these barriers through a more integrated approach. Key priorities should be given to developing inverse design models for dyes that simultaneously optimize multiple objectives (color, fastness, eco-toxicity), creating digital twin systems for holistic process control, and advancing quality systems from "post-process detection" to "in-process prediction" by fusing multi-modal data. Ultimately, the goal is to connect these disparate AI applications into a cohesive, closed-loop intelligent manufacturing system that spans the entire production chain. Achieving this vision will unlock AI's full potential to build a smarter, greener, and more competitive textile industry.

Key words: dyeing and finishing, artificial intelligence, dye design, color matching, process optimization, quality inspection, machine learning, deep learning

中图分类号:

TP18

周青青, 常硕, 毛志平, 吴伟. 人工智能在纺织印染行业中的应用研究进展[J]. 纺织学报, 2025, 46(12): 260-269.

ZHOU Qingqing, CHANG Shuo, MAO Zhiping, WU Wei. Research progress in applications of artificial intelligence in dyeing and finishing industry[J]. Journal of Textile Research, 2025, 46(12): 260-269.

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链接本文: http://www.fzxb.org.cn/CN/10.13475/j.fzxb.20250404402

http://www.fzxb.org.cn/CN/Y2025/V46/I12/260

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Research progress in applications of artificial intelligence in dyeing and finishing industry

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