Addressable conducting network를 통한 기계 학습 기반 탄소 섬유 복합재의 손상 감지 및 자가 회복에 대한 연구

Abstract

Carbon fiber reinforced polymer composites (CFRP) are widely used in automobile, aircraft, space vehicle and sports equipment because of their lightweight, high specific strength and high specific stiffness. Due to its benefits, many researchers are trying to replace the heavy metallic material of automobile components to lighter materials. Generally, CFRP has excellent mechanical properties in fiber direction. However, matrix cracking and delamination can be generated easily in structural application. These failures can cause serious accidents. So, it is necessary to develop damage detection and repair technique to prevent them. In this work, machine learning-based damage sensing system based on addressable conducting network (ACN) for CFRP was developed. ACN utilizes carbon fibers as conducting network, structural damage can be evaluated through by resistance change of composite itself. Moreover, an application of machine learning technique allows in-situ autonomic damage sensing once damage algorithm is fully established. In addition, an analytical model using the Kirchhoff's circuits laws was developed to investigate the resistance change due to damage of it. After damage sensing, self-healing of the carbon fiber composite material was performed by performing local heating using resistance heat. As a result, it was noteworthy that established machine learning algorithm based on ACN exhibited high accuracy damage sensing resolution under impact test. Furthermore, it was confirmed that self-healing was also successfully performed through ultrasound examination.