期刊
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
卷 -, 期 -, 页码 -出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/14759217221141764
关键词
Carbon fiber nylon composite; addressable conducting network; self-healing; machine learning and artificial neural network
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%.
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.
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