Document Type
Article
Source Publication Title
Proceedings of Society for the Advancement of Material and Process Engineering (SAMPE) Conference 2019
DOI
10.33599/nasampe/s.19.1608
Abstract
Composite materials are extending the horizons of designers in all branches of engineering. These materials have numerous advantages and improved structural properties such as high strength to weight ratio, high stiffness to weight ratio, lightweight, structural strength, and excellent durability. This has led to their use in several applications i.e. automobile, aircraft, and military defense devices. However, these materials experience various types of deformations and damage modes during their service life that are at times challenging to detect. This has led to the development of various non-destructive methods for structural health monitoring (SHM) of the damages in these complex material systems. There are different methods of SHM, which include both wired and wireless techniques. Most of current wireless sensing techniques use relatively large sensors, which are difficult to embed into the composites. This paper presents a small wireless sensor made from magnetostrictive materials that allows continuous monitoring of the local condition within the composites. This sensor can be either attached on the surface of the composites or embedded within the composites. The sensor response during the tensile loading on the composites is monitored. The wireless monitoring using the magnetostrictive sensor can be a convenient in-situ method for SHM of composite structures.
Disciplines
Engineering | Materials Science and Engineering
Publication Date
4-11-2019
Language
English
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Islam, Sujjatul; Qhobosheane, Relebohile George; Elenchezhian, Muthu Ram Prabhu; Vadlamudi, Vamsee; Raihan, Rassel; and Reifsnider, Kenneth, "Structural Health Monitoring of Fiber-Reinforced Composite Using Wireless Magnetostrictive Sensors" (2019). Institute of Predictive Performance Methodologies (IPPM-UTARI). 18.
https://mavmatrix.uta.edu/utari_ippm/18