This review focuses on the transformative applications of deep learning and artificial intelligence in textile dyeing, printing, and finishing. In textile dyeing, the topics span color prediction, color-based classification, dyeing recipe prediction, dyeing pattern recognition, and the nuanced domain of color fabric defect detection. In textile printing, applications of artificial intelligence and machine learning center around pattern detection in printed fabrics, the generation of novel patterns, and the critical task of detecting defects in printed textiles. In textile finishing the prediction of fabric thermosetting parameters is discussed. Artificial neural networks, diverse convolutional neural network variations like AlexNet, traditional machine learning approaches including support vector regression, principal component analysis, XGBoost, and generative artificial intelligence such as generative adversarial networks, as well as genetic algorithms all find application in this multifaceted exploration. At its core, the interest to use these methodologies is because of the need to minimize repetitive and time-consuming manual tasks, curtail prototyping costs, and promote process automation. The review unravels a plethora of innovative architectures and frameworks, each tailored to address specific challenges. However, a persistent hurdle looms – the scarcity of data, which remains a significant impediment. While unveiling a collection of research findings, the review also spotlights the inherent challenges in implementing artificial intelligence solutions in the textile dyeing and printing domain.
ShameyRHussainT.Artificial intelligence in the colour and textile industry. Rev Prog Color Relat Top2003;
33: 33–45.
2.
ZhuH.A Neural Network Model to Predict the Color of Dry Cotton Fabric from a Wet State.
Raleigh, USA:
North Carolina State University, 2022.
3.
LiuSLiuYKKanC.Using Bayesian regulated neural network (BRNN) to predict the effect of plasma treatment on the fading effect of cotton fabric. J Text Inst2023;
115(8): 1207–1217.
4.
ChenZZhouCZhouY, et al. Multi-dimensional Regression for Colour Prediction in Pad Dyeing. In: SunXPanZBertinoE (eds) Cloud Computing and Security.
Cham:
Springer International Publishing, 2018, pp. 675–687.
5.
XinBZhangJZhangR, et al.
Color texture classification of yarn-dyed woven fabric based on dual-side scanning and co-occurrence matrix. Text Res J2017;
87: 1883–1895.
6.
HeZTranKPThomasseyS, et al. In: ISSAT International Conference on Data Science in Business, Finance and Industry, July 2019, Da Nang, Vietnam.
7.
YuCXiZLuY, et al.
LSSVM-based color prediction for cotton fabrics with reactive pad-dry-pad-steam dyeing. Chemom Intell Lab Syst2020;
199: 103956.
8.
HajiAVadoodM.Environmentally benign dyeing of polyester fabric with madder: modelling by artificial neural network and fuzzy logic optimized by genetic algorithm. Fibers Polym2021;
22: 3351–3357.
9.
HeZTranK-PThomasseyS, et al.
A deep reinforcement learning based multi-criteria decision support system for optimizing textile chemical process. Comput Ind2021;
125: 103373.
10.
Omerogullari BasyigitZEyupogluCEyupogluS, et al.
Investigation and feed‐forward neural network‐based estimation of dyeing properties of air plasma treated wool fabric dyed with natural dye obtained from Hibiscus sabdariffa. Color Technol2023;
139: 441–453.
11.
FurferiRServiM.A machine vision-based algorithm for color classification of recycled wool fabrics. Appl Sci2023;
13: 2464.
12.
ZhangZSunFLiQ, et al.
Establishment of the predicting models of the dyeing effect in supercritical carbon dioxide based on the generalized regression neural network and back propagation neural network. Processes2020;
8: 1631.
13.
ZhouZLiuDGuoJ, et al.
Dyed fabric illumination estimation with regularized random vector function link network. Color Res Appl2021;
46: 376–387.
14.
AmelioABonifaziGCorradiniE, et al.
Defining a deep neural network ensemble for identifying fabric colors. Appl Soft Comput2022;
130: 109687.
15.
TuZYinYQinX. Towards Better Data Pre-Processing for Building Recipe Recommendation Systems from Industrial Fabric Dyeing Manufacturing Records: Categorization of Coloration Properties for a Dye Combination on Different Fabrics. In: Design Studies and Intelligence Engineering. IOS Press, 2022, Amsterdam, The Netherlands, pp. 17–23.
16.
PervezMdNYeoWSLinL, et al.
Optimization and prediction of the cotton fabric dyeing process using Taguchi design-integrated machine learning approach. Sci Rep2023;
13: 12363.
17.
WangC-CKuoC-H.Detecting dyeing machine entanglement anomalies by using time series image analysis and deep learning techniques for dyeing-finishing process. Adv Eng Inform2023;
55: 101852.
18.
ChaouchSMoussaABen MarzougI, et al.
Application of genetic algorithm to color recipe formulation using reactive and direct dyestuffs mixtures. Color Res Appl2020;
45: 896–910.
19.
ZhangJZhangXWuJ, et al.
Dyeing recipe prediction of cotton fabric based on hyperspectral colour measurement and an improved recurrent neural network. Color Technol2021;
137: 166–180.
20.
LiFChenCMaoZ.A novel approach for recipe prediction of fabric dyeing based on feature‐weighted support vector regression and particle swarm optimisation. Color Technol2022;
138: 495–508.
21.
QinXZhangJ.An industrial dyeing recipe recommendation system for textile fabrics based on data-mining and modular architecture design. IEEE Access2021;
9: 136105–136110.
22.
LiuSXingXWangS, et al.
Tie-dyeing pattern fast-generation method based on deep-learning and digital-image-processing technology. Autex Res J2023;
23: 474–482.
23.
LiFXiQ.Research and implementation of a fabric printing detection system based on a field programmable gate array and deep neural network. Text Res J2022;
92: 1060–1078.
24.
WuJLiPZhangH, et al.
CARL-YOLOF: A well-efficient model for digital printing fabric defect detection. J Eng Fibers Fabr2022;
17: 155892502211350.
25.
ŞahinCBalcıOIşıkM, et al.
Artificial neural networks approach for prediction of CIELab values for yarn after dyeing and finishing process. J Text Inst2023;
114: 1326–1335.
26.
YuCXiZLuY, et al.
K/S value prediction of cotton fabric using PSO-LSSVM. Text Res J2020;
90: 2581–2591.
27.
HajiAVadoodM.Prediction of color coordinates of cotton fabric dyed with binary mixtures of madder and weld natural dyes using artificial intelligence. Fibers Polym2023;
24: 1759–1769.
28.
VadoodMHajiA.A hybrid artificial intelligence model to predict the color coordinates of polyester fabric dyed with madder natural dye. Expert Syst Appl2022;
193: 116514.
29.
EyupogluCEyupogluSMerdanN, et al.
Natural dyeing of air plasma-treated wool fabric with Rubia tinctorum L. and prediction of dyeing properties using an artificial neural network. Color Technol2023;
140: 91–102.
30.
HajiAVadoodMÖztürkM, et al.
Prediction of colour strength in environmentally‐friendly dyeing of polyester fabric with madder using supercritical carbon dioxide. Color Technol2024: 1–10.
31.
KampourisCZafeiriouSGhoshA, et al. Fine-Grained Material Classification Using Micro-geometry and Reflectance. In: LeibeBMatasJSebeN, et al. (eds) Computer Vision – ECCV 2016.
Cham:
Springer International Publishing, pp. 778–792.
32.
WangC-CLiY-H.Machine-learning-based system for the detection of entanglement in dyeing and finishing processes. Sustainability2022;
14: 8575.
33.
LimJJeongSLimS, et al. Development of Dye Exhaustion Behavior Prediction Model using Deep Neural Network. In: YamashitaYKanoM (eds) Computer Aided Chemical Engineering.
Amsterdam, The Netherlands:
Elsevier, 2022, pp. 1825–1830.
34.
JeongSLimJIl HongS, et al.
A framework for environmental production of textile dyeing process using novel exhaustion-rate meter and multi-layer perceptron-based prediction model. Process Saf Environ Prot2023;
175: 99–110.
35.
ChenMTsangHSTsangKT, et al. A Hybrid Model CMR-Color of Automatic Color Matching Prediction for Textiles Dyeing and Printing. In: ZhangHYangZZhangZ, et al. (eds) Neural Computing for Advanced Applications.
Singapore:
Springer, 2021, pp. 603–618.
36.
SouissiMChaouchSMoussaA, et al.
Dyeing of advanced denim fabrics (blend of cotton/bicomponent polyester filaments) using different processes and artificial intelligence method. Sci Rep2024;
14: 1952.
37.
YangYMaHYangQ, et al.
Recipe prediction of mélange yarn using modular artificial neural network. J Text Inst2018;
109: 629–635.
38.
SunXXueYLiuY, et al.
A neural network algorithm and its prediction model towards the full color phase mixing process of colored fibers. Text Res J2023;
93: 2449–2463.
39.
SunXXueYLiuY, et al.
Neuronal network algorithm towards color model of full color gamut yarn and its color prediction research. Text Res J2023;
93: 2694–2707.
40.
BaruaSPatilHDesaiPD, et al. Deep Learning-Based Smart Colored Fabric Defect Detection System. In: IyerBRajurkarAMGudivadaV (eds) Applied Computer Vision and Image Processing.
Singapore:
Springer, 2020, pp. 212–219.
41.
LecunYBottouLBengioY, et al.
Gradient-based learning applied to document recognition. Proc IEEE1998;
86: 2278–2324.
42.
ChakrabortyS.Automatic Printed Fabric Defect Detection Using Convolutional Neural Network. Doctor of Philosophy Thesis, North Carolina State University, 2021.
VafeiadisTKolokasNDimitriouN, et al.
A comparison of 2DCNN network architectures and boosting techniques for regression-based textile whiteness estimation. Simul Model Pract Theory2022;
114: 102400.
45.
ZhangHXiongWLuS, et al.
QA-USTNet: Yarn-dyed fabric defect detection via U-shaped Swin Transformer Network based on Quadtree Attention. Text Res J2023;
93: 3492–3508.
46.
LiFXiQFengY.Rapid Detection Solution for Fabric Printing Based on Dual Architecture Design of FPGA and Neural Network. In: International Conference on Frontiers of Electronics, Information and Computation Technologies.
Changsha, China:
ACM, May 21-23, 2021 pp. 1–8.
47.
XiangZShenYChengZ, et al.
Periodic pattern detection of printed fabric based on deep learning algorithm. J Phys Conf Ser2022;
2148: 012013.
48.
XiaoH.Optimized soft frame design of traditional printing and dyeing process in Xiangxi based on pattern mining and edge-driven scene understanding. Soft Comput2022;
26: 12997–13008.
49.
WangSSunZ.Dyeing creation: a textile pattern discovery and fabric image generation method. Multimed Tools Appl2021;
80: 26511–26530.
50.
ZhuWWangZLiQ, et al.
A method of enhancing silk digital printing color prediction through Pix2Pix GAN-based approaches. Appl Sci2024;
14: 11.
51.
LiYZhangXYangL, et al.
Study of color reproduction in pigment digital printing. Text Res J2023;
93: 2718–2737.
IrshadFAshrafMFarooqA, et al.
Development of prediction system for shade change variations in dyed cotton fabric after application of water repellent finishes. J Nat Fibers2023;
20: 2154302.
55.
FarooqAIrshadFAzeemiR, et al.
Prognosticating the shade change after softener application using artificial neural networks. Autex Res J2020;
21: 79–84.
56.
MüllerKPosada-MorenoAPelzerL, et al. Objectifying Machine Setup and Parameter Selection in Expert Knowledge Dependent Industries Using Invertible Neural Networks. In: KimK-YMonplaisirLRickliJ (eds) Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus.
Cham:
Springer International Publishing, 2023, pp. 293–300.
57.
TadesseMGLoghinEPislaruM, et al.
Prediction of the tactile comfort of fabrics from functional finishing parameters using fuzzy logic and artificial neural network models. Text Res J2019;
89: 4083–4094.
58.
HesarianMSTavoosiJ.Evaluating the smoothness of the washed fabric after laundry with the washing machine based on a new type-2 fuzzy neural network. Complexity2022;
2022: e2401736.
59.
AhirwarMBeheraBK.Prediction of shrinkage behavior of stretch fabrics using machine-learning based artificial neural network. Textiles2023;
3: 88–97.
60.
TongYWangQWangZ, et al.
Prediction of parameters in the laser fading process of denim using convolutional neural networks. Text Res J2023;
93: 3790–3801.
61.
LeeWChoiHKSajadiehSMM, et al. Prediction of Residual Dye Using Machine Learning Algorithms for an Eco-Friendly Dyeing Process. In: AlfnesERomsdalAStrandhagenJO, et al. (eds) Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures.
Cham:
Springer Nature Switzerland, 2023, pp. 491–505.
62.
YuanLGongXLiuJ, et al.
Colored spun fabric texture representation and application by combining spatial features with frequency features. Text Res J2021;
91: 828–838.
63.
WangYLiWWangJ.Color prediction model for hybrid multifilament fabric. Text Res J2022;
92: 1038–1048.
64.
WangKWangJGaoW.Enhancing warp sizing effect and quality: a comprehensive review of the squeezing process and future research. Text Res J2024;
0: 00405175241235400.
