ML-Enhanced Self-Healing Fiber-Reinforced Polymer Composites with Embedded IoT Sensors for Damage Prediction

Fiber-reinforced polymer (FRP) composites are widely used in aerospace and structural systems;
nevertheless, the potential for microcracking and fatigue-induced performance degradation remains an
obstacle with respect to improved service life. Traditional self-healing methods, while performing well
on a chemical level, often lack real-time diagnostic awareness and adaptive control. To circumvent
this, we developed a machine-learning augmented self-healing FRP composite, in which a DCPD–
Grubbs catalytic matrix was combined with IoT sensor network capability and a hybrid CNN–LSTM
predictive model. This framework identifies, interprets, and performs in-situ actions, enabling the
material to become an intelligent closed-loop system capable of self-healing and managing damage
automatically instead of passively. Our work differs from previous studies, which focus on the simulated
static analysis of FRP composites.