Journal article
Weaving intelligence: thermally drawn multimaterial fibers toward AI‐enabled smart textiles
- Abstract:
- The rapid evolution of smart textiles is intensifying demand for multimaterial systems that couple mechanical compliance with embedded, adaptive computational capabilities. Thermally drawn fibers have emerged as a powerful platform, enabling the co‐integration of polymers, metals, semiconductors, and piezoelectric or iontronic phases into continuous multimaterial architectures with high geometric fidelity and manufacturing scalability. These hybrid fibers enable distributed sensing, energy modulation, and signal transduction, while generating high‐dimensional data streams well suited for artificial intelligence (AI)‐driven analysis. This review surveys recent advances at the intersection of AI and thermal drawing technologies, including data‐centric optimization, real‐time process control, signal processing, and predictive modeling, which are reshaping both fiber fabrication and system‐level integration. We highlight progress in multimaterial co‐drawing, hierarchical fiber engineering, and functionally integrated architectures that establish the foundation for in‐fiber computation. Emphasis is placed on neuromorphic and spiking neural network (SNN)–based approaches, which enable energy‐efficient, event‐driven computation aligned with the distributed and deformable nature of textile platforms. Finally, we discuss emerging challenges and opportunities, including scalable neuromorphic architectures, uncertainty‐aware learning, and AI‐driven materials optimization. Together, these advances outline a pathway toward autonomous, self‐optimizing textile systems in which individual fibers function as distributed, cognitively inspired nodes within next‐generation intelligent materials.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Version of record, pdf, 20.2MB, Terms of use)
-
- Publisher copy:
- 10.1002/adma.73574
Authors
+ Vingroup Innovation Foundation
More from this funder
- Funder identifier:
- https://ror.org/05cz3nq82
- Grant:
- VinIF.2023.DA.070
+ Center for Environmental Intelligence, VinUniversity
More from this funder
- Funder identifier:
- https://ror.org/052dmdr17
- Grant:
- VUNI.CEI.FS_0007
- Publisher:
- Wiley
- Journal:
- Advanced Materials More from this journal
- Volume:
- 38
- Issue:
- 40
- Article number:
- e73574
- Publication date:
- 2026-06-05
- Acceptance date:
- 2026-05-22
- DOI:
- EISSN:
-
1521-4095
- ISSN:
-
0935-9648
- Language:
-
English
- Keywords:
- Pubs id:
-
2448634
- Local pid:
-
pubs:2448634
- Source identifiers:
-
W7163716558
- Deposit date:
-
2026-08-11
- ARK identifier:
Terms of use
- Copyright holder:
- Trung et al.
- Copyright date:
- 2026
- Rights statement:
- © 2026 The Author(s). Advanced Materials published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
If you are the owner of this record, you can report an update to it here: Report update to this record