Dr. Ramesh Sundaram Profile

Dr. Ramesh Sundaram

Scientist, Advanced Composites Unit at CSIR-National Aerospace Labs

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Publications (2)

Proceedings Article | 24 August 2011 Paper

Load tracking and structural health monitoring of unmanned aerial vehicles using optical fiber sensors

A. Handelman, Y. Botsev, J. Balter, P. Gud's, I. Kressel, M. Tur, S. Gali, A. C. R. Pillai, M. Hari Prasad, A. Kumar Yadav, Nitesh Gupta, Sakthi Sathya, Ramesh Sundaram

Proceedings Volume 8173, 81731Z (2011) https://doi.org/10.1117/12.898946

KEYWORDS: Fiber Bragg gratings, Unmanned aerial vehicles, Sensors, Structural health monitoring, Fiber optics sensors, Composites, Optical tracking, Optical fibers, Manufacturing, Position sensors

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Proceedings Article | 17 May 2005 Paper

Structural health monitoring of co-cured composite structures using FBG sensors

Ramesh Sundaram, G. Kamath, Nitesh Gupta, M. Subba Rao

Proceedings Volume 5764, (2005) https://doi.org/10.1117/12.597261

KEYWORDS: Fiber Bragg gratings, Skin, Sensors, Composites, Seaborgium, Finite element methods, Structural health monitoring, Neural networks, Aircraft structures, Neurons

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Structural Health Monitoring (SHM) of aircraft structures, especially composite structures, has assumed increased significance on considerations of safety and costs. With the advent of co-cured structures, wherein bonded joints are replacing bolted joints there is a concern regarding skin-stiffener separation, which might not be detected unless a rigorous non-destructive testing (NDT) is done. It would hence be necessary to be able to detect and assess skin-stiffener separation in composite structures before it reaches the critical size. One of the health monitoring strategies is through strain monitoring using fibre optic strain sensors such as Fibre Bragg Grating (FBG) sensors. The first aspect that needs to be addressed is the characterization of the FBG sensors. Issues of embedment in composites have also to be addressed. Before evolving a damage detection strategy, the sensitivity of the structural strain to skin-stiffener separations must be clearly understood and quantified. This paper presents the analysis and experiments done with a composite test box to study the effect of skin-stiffener separation on the strain behaviour. The box consists of two skins stiffened with spars made of Bi-Directional (BD) glass-epoxy prepreg material. The spars are bolted to the skins and removing suitable number of bolts simulates 'de-bonds'. The strains of the healthy box are compared with the unhealthy box. The strains in the experiments are monitored using both strain gauges and Fibre Bragg Grating (FBG) sensors. The experimental results show that there is significant change in the measured strain near and away from the debond location. The finite element analysis of the box is done using ABAQUS and the analysis is validated with the experimental results. A neural network based methodology is developed here to detect skin-stiffener debonds in structures. A multi-layer perceptron (MLP) neural network with a feed forward back propagation algorithm is used to determine the size/severity of damage. The FE model is used to generate the neural network training data for various sizes of debonds. The results show that the network is able to predict the damage size well. The network is implemented for a specified load. However, it is seen that the damage size predicted is independent of the applied load and the network performance is dependent on the fidelity of the finite element model used to train the network.

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