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

In this work, addressable conducting network (ACN) was used for the damage sensing and self-healing of continuous carbon fiber reinforced nylon composite (CFRP). The machine-learning was used for accurate damage sensing by training the resistance change of composites along ACN due to their structural damage. Also, self-healing of the carbon fiber composite material was performed by applying the electrical current to generate local heating through the detected damage location. The self-healing conditions such as the current input pairs of ACN and amount of the electrical current were determined through the artificial neural network (ANN)-based machine-learning technique. As a result, high-accuracy damage sensing based on machine learning with ACN was conducted, and self-healing with a healing efficiency of 98% could be achieved.

Automated summary

Addressable conducting network (ACN) was used for damage sensing and self-healing of continuous carbon fiber reinforced nylon composite (CFRP). Machine learning was employed to accurately sense damage and generate local heating for self-healing. Self-healing conditions were determined using artificial neural network (ANN) based machine learning, achieving a healing efficiency of 98%.