TY - GEN
T1 - CNT enabled fabric sensors for highly sensitive and large-area monitoring of polymeric composites
AU - Wang, Yong
AU - Zhai, Yujiang
AU - Wang, Guantao
AU - Luo, Sida
PY - 2017
Y1 - 2017
N2 - The next-generation of hierarchical composites needs to have built-in functionality to continually monitor and diagnose their own health states. This paper presents a novel strategy for in-situ monitoring the processing stages of composites by co-braiding CNT-enabled fiber sensors into the reinforcing fiber fabrics. This would tremendously improve present methods that excessively focus on detecting mechanical deformations and cracks. The CNT enabled smart fabrics, fabricated by a cost-effective and scalable method, are highly sensitive to monitor and quantify various events of composite processing including resin infusion, onset of crosslinking, gel time, degree and rate of curing. By varying curing temperature and resin formulation, the clear trends derived from systematic statistics confirm the reliability and accuracy of the method. More importantly, localized processing information of composites can be achieved in real time upon wisely configuring the smart fabrics with a scalable sensor network. In addition, the smart fabrics which are readily and non-invasively integrated into composites can provide lifelong structural health monitoring of the composites, including detection of deformations and cracks.
AB - The next-generation of hierarchical composites needs to have built-in functionality to continually monitor and diagnose their own health states. This paper presents a novel strategy for in-situ monitoring the processing stages of composites by co-braiding CNT-enabled fiber sensors into the reinforcing fiber fabrics. This would tremendously improve present methods that excessively focus on detecting mechanical deformations and cracks. The CNT enabled smart fabrics, fabricated by a cost-effective and scalable method, are highly sensitive to monitor and quantify various events of composite processing including resin infusion, onset of crosslinking, gel time, degree and rate of curing. By varying curing temperature and resin formulation, the clear trends derived from systematic statistics confirm the reliability and accuracy of the method. More importantly, localized processing information of composites can be achieved in real time upon wisely configuring the smart fabrics with a scalable sensor network. In addition, the smart fabrics which are readily and non-invasively integrated into composites can provide lifelong structural health monitoring of the composites, including detection of deformations and cracks.
UR - https://www.scopus.com/pages/publications/85032392767
U2 - 10.12783/shm2017/14067
DO - 10.12783/shm2017/14067
M3 - 会议稿件
AN - SCOPUS:85032392767
T3 - Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance - Proceedings of the 11th International Workshop on Structural Health Monitoring, IWSHM 2017
SP - 1850
EP - 1857
BT - Structural Health Monitoring 2017
A2 - Chang, Fu-Kuo
A2 - Kopsaftopoulos, Fotis
PB - DEStech Publications
T2 - 11th International Workshop on Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance, IWSHM 2017
Y2 - 12 September 2017 through 14 September 2017
ER -