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Machine Learning in Polymeric Technical Textiles: A Review.

Ivan Malashin1, Dmitry Martysyuk1, Vadim Tynchenko1

  • 1AI Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, Russia.

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This summary is machine-generated.

Machine learning (ML) and artificial intelligence (AI) are revolutionizing technical textiles by enabling efficient polymer design and prediction of material properties. These technologies enhance functionality, leading to advancements in smart textiles and sustainable material development.

Keywords:
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Area of Science:

  • Materials Science and Engineering
  • Polymer Science
  • Textile Technology
  • Artificial Intelligence
  • Machine Learning

Background:

  • Polymeric materials are crucial for technical textiles across diverse industries like healthcare, aerospace, automotive, and construction.
  • Traditional methods for material design and performance prediction are often time-consuming and resource-intensive.
  • The need for advanced, functional, and sustainable textile solutions necessitates innovative approaches.

Purpose of the Study:

  • To review the transformative impact of machine learning (ML) and artificial intelligence (AI) on the development of advanced polymeric materials for technical textiles.
  • To highlight how ML/AI facilitate efficient polymer design, property prediction, and the creation of smart, responsive textiles.
  • To provide guidance for future research directions in AI/ML-driven technical textile innovation.

Main Methods:

  • Review of existing literature and case studies on the application of ML and AI in polymer-based technical textiles.
  • Analysis of ML/AI contributions to material design, property prediction, defect detection, and smart wearable systems.
  • Examination of performance metrics, including classification accuracy, prediction error, and response times.

Main Results:

  • ML/AI achieve high accuracy (up to 100%) in waste sorting of pure fibers and predict material stiffness within 10% error.
  • AI enables defect prediction in fabric production for proactive interventions and development of smart wearable systems with rapid response times (192 ms for physiological monitoring).
  • Integration of AI technologies drives sustainable innovation and enhances the overall functionality of textile products.

Conclusions:

  • Machine learning and AI are powerful tools for accelerating the design and optimization of polymers for technical textiles.
  • These technologies significantly enhance the performance, functionality, and sustainability of advanced textile materials and systems.
  • Continued research and application of AI/ML are essential for future advancements in the field of polymer-based technical textiles.