BackgroundNow, it is AI and ML which are changing how we dye textiles, introducing greater automation, accuracy, andsustainability to the process. Traditional dyeing processes are frequently embodied by resources consumption,poor quality and test and error based. AI Technologies offer Data driven optimisation, real-time monitoring andcompliance to strictening environmental laws.MethodsThis article was written conceptually and analytically, as the authors had conducted a study of the current statusof AI application in dyeing. The technologies analysed are computer vision to detect defects, a digital twin tosimulate processes, reinforcement learning for control flexibility, and predictive maintenance for machines.Examples of measurable impacts are reviewed from case studies, industry reports and academic literature.ResultsThe study demonstrates a considerable advantage of AI-tool integration into dyeing. This leads to FTR-rates ofapproximately 20% within their color information and a shade accuracy of over 95% at low water and energyconsumption of up to 30%. Computer vision allows for defect identification beyond humans' capabilities, whiledigital twins decrease the risk of repassivation. Adaptability is increased with reinforcement learning, whiledowntime is reduced by over 60% through predictive maintenance. Nevertheless, certain difficulties remain interms of data access, the complexity of integration, and the required initial outlay.ConclusionAI and MLare revolutionizing the dyeing of textiles, making it an accurate, intelligent and sustainable process.The ability of AI-driven dyeing to overcome adoption barriers and through collaboration between industry,academia and policy makers deliver sustainable and competitive eco-efficient manufacturing. Thisinvestigation offers a roadmap for researchers and industrial professionals to enter the next-generation ofsmart textile fabrication.
Gurusamy et al. (Thu,) studied this question.