title: CNT Enabled Co-braided Smart Fabrics: A New Route for Non-invasive, Highly Sensitive &Large-area Monitoring of Composites.
abstract: The next-generation of hierarchical composites needs to have built-in functionality to continually monitor and diagnose their own health states. This paper includes 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 present a tremendous improvement over the 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 the systematic study confirm the reliability and accuracy of the method, which is further verified by rheological and DSC tests. More importantly, upon wisely configuring the smart fabrics with a scalable sensor network, localized processing information of composites can be achieved in real time. In addition, the smart fabrics that are readily and non-invasively integrated into composites can provide life-long structural health monitoring of the composites, including detection of deformations and cracks.
authors.affiliation: Beihang University, School of Mechanical Engineering &Automation, International Research Institute for Multidisciplinary Science, Beijing, 100191, China.|Florida State University, High Performance Materials Institute, Tallahassee, 32310, USA.
authors.forename: Sida
authors.initials: S
authors.lastname: Luo
authors.affiliation: Beihang University, School of Mechanical Engineering &Automation, International Research Institute for Multidisciplinary Science, Beijing, 100191, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Beihang University, School of Mechanical Engineering &Automation, International Research Institute for Multidisciplinary Science, Beijing, 100191, China.|China University of Geosciences, Center of Safety Research, Beijing, 100083, China.
authors.forename: Guantao
authors.initials: G
authors.lastname: Wang
authors.affiliation: Georgia Institute of Technology, H. Milton Stewart School of Industrial and Systems Engineering, Atlanta, 30332, USA.
authors.forename: Kan
authors.initials: K
authors.lastname: Wang
authors.affiliation: Stanford University, School of Medicine, Stanford, 94305, USA.
authors.forename: Zhibin
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Georgia Institute of Technology, H. Milton Stewart School of Industrial and Systems Engineering, Atlanta, 30332, USA.
authors.forename: Chuck
authors.initials: C
authors.lastname: Zhang
authors.affiliation: Georgia Institute of Technology, H. Milton Stewart School of Industrial and Systems Engineering, Atlanta, 30332, USA.
authors.forename: Ben
authors.initials: B
authors.lastname: Wang
authors.affiliation: China University of Geosciences, Center of Safety Research, Beijing, 100083, China.
authors.forename: Yun
authors.initials: Y
authors.lastname: Luo
authors.affiliation: Beihang University, School of Mechanical Engineering &Automation, International Research Institute for Multidisciplinary Science, Beijing, 100191, China.
authors.forename: Liuhe
authors.initials: L
authors.lastname: Li
authors.affiliation: Florida State University, High Performance Materials Institute, Tallahassee, 32310, USA.
authors.forename: Tao
authors.initials: T
authors.lastname: Liu