Malashin Ivan, Martysyuk Dmitry, Tynchenko Vadim, Gantimurov Andrei, Nelyub Vladimir, Borodulin Aleksei, Galinovsky Andrey
AI Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia.
Scientific Department, Far Eastern Federal University, 690922 Vladivostok, Russia.
Polymers (Basel). 2025 Apr 25;17(9):1172. doi: 10.3390/polym17091172.
The integration of machine learning (ML) has begun to reshape the development of advanced polymeric materials used in technical textiles. Polymeric materials, with their versatile properties, are central to the performance of technical textiles across industries such as healthcare, aerospace, automotive, and construction. By utilizing ML and AI, researchers are now able to design and optimize polymers for specific applications more efficiently, predict their behavior under extreme conditions, and develop smart, responsive textiles that enhance functionality. This review highlights the transformative potential of ML in polymer-based textiles, enabling advancements in waste sorting (with classification accuracy of up to 100% for pure fibers), material design (predicting stiffness properties within 10% error), defect prediction (enabling proactive interventions in fabric production), and smart wearable systems (achieving response times as low as 192 ms for physiological monitoring). The integration of AI technologies drives sustainable innovation and enhances the functionality of textile products. Through case studies and examples, this review provides guidance for future research in the development of polymer-based technical textiles using AI and ML technologies.
机器学习（ML）的整合已开始重塑用于技术纺织品的先进高分子材料的发展。高分子材料具有多种特性，是医疗保健、航空航天、汽车和建筑等行业技术纺织品性能的核心。通过利用ML和人工智能，研究人员现在能够更高效地为特定应用设计和优化聚合物，预测它们在极端条件下的行为，并开发出增强功能性的智能、响应性纺织品。本综述强调了ML在基于聚合物的纺织品中的变革潜力，实现了在废物分类（纯纤维分类准确率高达100%）、材料设计（预测刚度特性误差在10%以内）、缺陷预测（在织物生产中实现主动干预）和智能可穿戴系统（生理监测响应时间低至192毫秒）方面的进展。人工智能技术的整合推动了可持续创新，并增强了纺织产品的功能。通过案例研究和示例，本综述为未来利用人工智能和ML技术开发基于聚合物的技术纺织品的研究提供了指导。
