Header menu link for other important links
Structural health monitoring of aerospace composites
V. Rahul, S. Alokita, , V.R. Kar, , M. Manikandan,
Published in Elsevier
Pages: 33 - 52
The health monitoring of aerostructures assists performance enhancement of existing structures. Continuous monitoring and different techniques involved in the structural monitoring help to increase the efficiency of structures, postpone the failures, and provide the prototype for future aerospace structures with better durability. Structural performance of aerospace composites depends on strength, stiffness, yield capacity, bending capacity, resistance against corrosion, impact and lightning, and fatigue due to cyclic loading. In structural monitoring, the four different stages followed to monitor any damage in aerospace composite structure are operation evaluation, data accession, feature extraction, followed by statistical modeling. This chapter on structural health monitoring for aerostructures elaborates the methods to detect and prevent the failures in the structures, as observed through a series of literature available based on the type of damages and techniques to detect them like cracking, fiber pullout, delamination and shearography, eddy current method, transient thermographic method, etc, respectively. In this chapter structural health monitoring of composite aerostructures is reviewed in detail. Different techniques used to monitor the various failures occurring in the composite structures in aerospace industry are explained in detail. Structures made of composite material used in aerospace fail due to fiber-matrix damage. Hence, it is important to analyze such damage like fiber buckling, fiber splitting, fiber cracking, fiber fracture, and fiber bending, and cracks in the matrix etc. to prevent catastrophic results. © 2019 Elsevier Ltd. All rights reserved.
About the journal
JournalData powered by TypesetStructural Health Monitoring of Biocomposites, Fibre-Reinforced Composites and Hybrid Composites
PublisherData powered by TypesetElsevier