Machine learning-based damage sensing and self-healing of carbon fiber/nylon composites via addressable conducting networks

Myeong-Hyeon Yu, Ji-Seok Lee, Hak‐Sung Kim

Structural Health Monitoring 2023 article cited by 10

Source More in Engineering & Materials

importance 4/10 partially solved Polymer composites and self-healing composite materials stage: research

Problem

Detecting structural damage in carbon fiber reinforced nylon composites and healing the detected damage.

Why it matters

Undetected damage in composite structures can lead to failure; in-situ sensing and healing extend material reliability.

What the AI did

Machine learning trained on resistance changes along an addressable conducting network sensed damage, and an ANN determined the current input conditions for self-healing via local heating.

Results

Self-healing with a healing efficiency of 98% was achieved; damage sensing described as high-accuracy but no sensing accuracy number given.

Impact

The evidence supports a demonstrated ML-guided damage sensing and self-healing workflow with 98% healing efficiency in the tested composite.

Record

Venue	Structural Health Monitoring
Published	2023
Work type	article
Authors	Myeong-Hyeon Yu, Ji-Seok Lee, Hak‐Sung Kim
Citations	10 (OpenAlex, at import time)
References	18
Model or method	artificial neural network (ANN)-based machine learning
Organization	not stated in source
Primary source	https://doi.org/10.1177/14759217221141764
Open access	closed
Keywords	carbon fiber composite, damage sensing, self-healing, machine learning, addressable conducting network

Importance rating

4 / 10

real but narrower advance

Lab-scale demonstration of ML-guided damage sensing and 98% healing efficiency in one composite type; real but narrow materials advance with no stated scale.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

More in Engineering & Materials

#	Paper	Year	Citations	Importance
1	Scaling deep learning for materials discovery Amil Merchant, Simon Batzner, Samuel S. Schoenholz, Muratahan Aykol, G...	2023	1,471	8
2	Application of Machine Learning in Material Synthesis and Property Prediction Guannan Huang, Yani Guo, Ye Chen, Zhengwei Nie	2023	165	7
3	Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries Kai Li, Jifeng Wang, Yuanyuan Song, Ying Wang	2023	115	7
4	Machine Learning for Structural Health Monitoring of Aerospace Structures: A Review Gennaro Scarselli, Francesco Nicassio	2025	44	7
5	Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology Mihail D. Senyuk, Murodbek Safaraliev, Firuz Kamalov, Hana Sulieman	2023	42	7