Prof. Mannur J. Sundaresan Profile

Prof. Mannur J. Sundaresan

SDL Author at North Carolina A&T State Univ

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Author

Publications (40)

Proceedings Article | 1 April 2019 Presentation + Paper

Identifying transition of fatigue cracks from tensile to shear mode based on acoustic emission signals

Kassahun Asamene, Duy Tran, Mannur Sundaresan

Proceedings Volume 10971, 109711L (2019) https://doi.org/10.1117/12.2518737

KEYWORDS: Acoustic emission, Wave propagation, Signal detection, Sensors, Aluminum, Wave plates, Transducers, Structural health monitoring, Wavelets, Signal generators

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Proceedings Article | 1 April 2019 Presentation + Paper

Measurement of stress wave attenuation in composite laminates

Duy Tran, Kassahun Asamene, Mannur Sundaresan

Proceedings Volume 10971, 109711K (2019) https://doi.org/10.1117/12.2518278

KEYWORDS: Signal attenuation, Composites, Semiconducting wafers, Signal detection, Acoustic emission, Structural health monitoring, Sensors, Wave plates, Wave propagation, Signal to noise ratio

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Proceedings Article | 1 April 2019 Paper

Measure of in-plane thermal conductivity of composite structures

Manoj Rijal, Letchuman Sripragash, Mannur Sundaresan

Proceedings Volume 10971, 1097121 (2019) https://doi.org/10.1117/12.2518277

KEYWORDS: Composites, Thermography, Diffusion, Numerical analysis, Thermal modeling, Heat flux, Data processing, Infrared cameras, MATLAB, Numerical simulations

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Proceedings Article | 29 March 2019 Presentation

Acoustic emission monitoring of rate of damage growth in composite structures (Conference Presentation)

Duy Tran, Mannur Sundaresan

Proceedings Volume 10973, 109730L (2019) https://doi.org/10.1117/12.2518273

KEYWORDS: Acoustic emission, Composites, Structured optical fibers, Sensors, Failure analysis, Analytical research, Acoustics

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Proceedings Article | 17 April 2013 Paper

Actual working mechanisms of smart metamaterial structures

P. Frank Pai, Mannur Sundaresan

Proceedings Volume 8695, 86952M (2013) https://doi.org/10.1117/12.2009792

KEYWORDS: Metamaterials, Acoustics, Wave propagation, Finite element methods, Absorption, Beam propagation method, Manufacturing, Electromagnetism, Numerical simulations, Electromagnetic radiation

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Here we present methods for modeling, analysis, and design of metamaterial beams for broadband vibration absorption/isolation. The proposed metamaterial beam consists of a uniform isotropic beam with many small spring-mass- damper subsystems integrated at separated locations along the beam to act as vibration absorbers. For a unit cell of an infinite metamaterial beam, governing equations are derived using the extended Hamilton principle. The existence of stopband is demonstrated using a model based on averaging material properties over a cell length and a model based on finite element modeling and the Bloch-Floquet theory for periodic structures. However, these two idealized models cannot be used for finite beams and/or elastic waves having short wavelengths. For finite metamaterial beams, a linear finite element method is used for detailed modeling and analysis. Both translational and rotational absorbers are considered. Because results show that rotational absorbers are not efficient, only translational absorbers are recommended for practical designs. The concepts of negative effective mass and stiffness and how the spring-mass-damper subsystems create a stopband are explained in detail. Numerical simulations reveal that the actual working mechanism of the proposed metamaterial beam is based on the concept of conventional mechanical vibration absorbers. It uses the incoming elastic wave in the beam to resonate the integrated spring-mass-damper absorbers to vibrate in their optical mode at frequencies close to but above their local resonance frequencies to create shear forces and bending moments to straighten the beam and stop the wave propagation. This concept can be easily extended to design a broadband absorber that works for elastic waves of short and long wavelengths. Numerical examples validate the concept and show that, for high-frequency waves, the structure’s boundary conditions do not have significant influence on the absorbers’ function. However, for absorption of low-frequency waves, the boundary conditions and resonant modes of the structure need to be considered in the design. With appropriate design calculations, finite discrete spring-mass- damper absorbers can be used, and hence expensive micro- or nano-manufacturing techniques are not needed for design and manufacturing of such metamaterial beams for broadband vibration absorption/isolation.

Proceedings Article | 17 April 2013 Paper

Dynamic characteristics and vibration-based damage inspection of structures with actual fatigue cracks

P. Frank Pai, Jun Liu, Mannur Sundaresan

Proceedings Volume 8695, 869533 (2013) https://doi.org/10.1117/12.2009722

KEYWORDS: Inspection, Time-frequency analysis, Signal processing, Data modeling, System identification, Complex systems, Temporal resolution, Data processing, Imaging systems, Dynamical systems

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Proceedings Article | 16 April 2013 Paper

Monitoring damage development around stress raisers in carbon/epoxy laminates

L. Sripragash, C. Smith, G. Kumaran, M. Sundaresan

Proceedings Volume 8694, 869404 (2013) https://doi.org/10.1117/12.2009858

KEYWORDS: Thermography, Wave propagation, Composites, Thermal modeling, Nondestructive evaluation, Vibrometry, Numerical simulations, Lamps, Diffusion, Pulsed laser operation

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Proceedings Article | 11 April 2013 Paper

Monitoring friction related surface degradation using acoustic emission technique

B. Mills-Dadson, ABM Islam, K. Asamene, M. Sundaresan

Proceedings Volume 8694, 86940V (2013) https://doi.org/10.1117/12.2009860

KEYWORDS: Acoustic emission, Surface roughness, Signal processing, Interfaces, Sensors, Signal generators, Amplifiers, Ferroelectric materials, Electronic filtering, Mechanical engineering

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Proceedings Article | 11 April 2013 Paper

Monitoring damage growth in carbon/epoxy composite panels

T. Whitlow, K. Asamene, L. Hudson, A. Parker, M. Sundaresan

Proceedings Volume 8694, 86940U (2013) https://doi.org/10.1117/12.2009847

KEYWORDS: Composites, Signal attenuation, Failure analysis, Sensors, Acoustic emission, Structural health monitoring, Carbon, Acousto-optics, Ferroelectric materials, Signal generators

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Proceedings Article | 20 April 2012 Paper

Identification of nonlinearities for damage inspection of thin-walled structures

P. Frank Pai, Mannur Sundaresan, Bao Anh Nguyen

Proceedings Volume 8348, 83482F (2012) https://doi.org/10.1117/12.914399

KEYWORDS: Time-frequency analysis, Complex systems, Signal processing, Modulation, System identification, Dynamical systems, Fourier transforms, Nonlinear optics, Inspection, Wavelet transforms

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This paper shows that time-frequency analysis is most appropriate for nonlinearity identification, and presents advanced signal processing techniques that combine time-frequency decomposition and perturbation methods for parametric and non-parametric identification of thin-walled structures and other dynamical systems. Hilbert-Huang transform (HHT) is a recent data-driven adaptive time-frequency analysis technique that combines the use of empirical mode decomposition (EMD) and Hilbert transform (HT). Because EMD does not use predetermined basis functions and function orthogonality for component extraction, HHT provides more concise component decomposition and more accurate timefrequency analysis than the short-time Fourier transform and wavelet transform for extraction of system characteristics and nonlinearities. However, HHT's accuracy seriously suffers from the end effect caused by the discontinuity-induced Gibbs' phenomenon. Moreover, because HHT requires a long set of data obtained by high-frequency sampling, it is not appropriate for online frequency tracking. This paper presents a conjugate-pair decomposition (CPD) method that requires only a few recent data points sampled at a low frequency for sliding-window point-by-point adaptive timefrequency analysis and can be used for online frequency tracking. To improve adaptive time-frequency analysis, a methodology is developed by combining HHT and CPD for noise filtering in the time domain, reducing the end effect, and dissolving other mathematical and numerical problems in time-frequency analysis. For parametric identification of a nonlinear system, the methodology processes one steady-state response and/or one free damped transient response and uses amplitude-dependent dynamic characteristics derived from perturbation analysis to determine the type and order of nonlinearity and system parameters. For non-parametric identification, the methodology uses the maximum displacement states to determine the displacement-stiffness curve and the maximum velocity states to determine the velocity-damping curve. Numerical simulations and experimental verifications of several nonlinear discrete and continuous systems show that the proposed methodology can provide accurate parametric and non-parametric identifications of different nonlinear dynamical systems.

Proceedings Article | 5 April 2012 Paper

Acoustic emission-based monitoring of surfaces subjected to friction

Kassahun Asamene, Mannur Sundaresan

Proceedings Volume 8347, 834712 (2012) https://doi.org/10.1117/12.915446

KEYWORDS: Acoustic emission, Surface roughness, Surface finishing, Sensors, Corrosion, Wavelets, Signal generators, Signal processing, Radium, Wavelet transforms

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Proceedings Article | 20 April 2011 Paper

Physics-based classification of acoustic emission waveforms

Duwarahan Rajendra, Travis Knighton, Albert Esterline, Mannur Sundaresan

Proceedings Volume 7983, 79833H (2011) https://doi.org/10.1117/12.880668

KEYWORDS: Acoustic emission, Sensors, Signal generators, Feature extraction, Interference (communication), Physics, Signal to noise ratio, Feature selection, Computer simulations, Structural health monitoring

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Proceedings Article | 18 April 2011 Paper

Time-frequency and space-wavenumber analysis for damage inspection of thin-walled structures

P. Frank Pai, Mannur Sundaresan

Proceedings Volume 7984, 79842J (2011) https://doi.org/10.1117/12.880329

KEYWORDS: Time-frequency analysis, Inspection, Signal processing, Transform theory, Damage detection, Data modeling, Fourier transforms, Temporal resolution, Time metrology, Dynamical systems

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Proceedings Article | 15 April 2011 Paper

Experimental analysis of fretting related acoustic emission signals

Kassahun Asamene, Wesley Williams, Mannur Sundaresan

Proceedings Volume 7981, 79813V (2011) https://doi.org/10.1117/12.880583

KEYWORDS: Acoustic emission, Radium, Signal generators, Surface roughness, Sensors, Signal processing, Structural health monitoring, Surface finishing, Numerical simulations, Polishing

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Proceedings Article | 15 April 2011 Paper

Structural health monitoring of shear waves in aluminum plates

Mannur Sundaresan, Wesley Williams

Proceedings Volume 7982, 79820Z (2011) https://doi.org/10.1117/12.880584

KEYWORDS: Sensors, Acoustic emission, Aluminum, Wave plates, Structural health monitoring, Wave propagation, Copper, Sensor performance, Adhesives, Lead

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Proceedings Article | 1 April 2011 Paper

Vibration characteristics of shear loaded post-buckled aluminum panels

B. Ali, L. Sripragash, M. Sundaresan

Proceedings Volume 7984, 79841I (2011) https://doi.org/10.1117/12.880582

KEYWORDS: Aluminum, Structural health monitoring, Numerical analysis, Safety, Shape analysis, Ferroelectric materials, Chemical elements, Alternate lighting of surfaces, Mechanical engineering, Wind turbine technology

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Proceedings Article | 9 April 2010 Paper

Characterization of fretting related acoustic emission signals

Md. Tariqul Alam, Mannur Sundaresan

Proceedings Volume 7650, 76500J (2010) https://doi.org/10.1117/12.847786

KEYWORDS: Acoustic emission, Surface finishing, Composites, Signal generators, Surface roughness, Signal processing, Sensors, Structural health monitoring, Electronic filtering, Linear filtering

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Proceedings Article | 9 April 2010 Paper

Structural health monitoring techniques for detecting incipient buckling

Kassahun Asamene, Bashir Ali, Mannur Sundaresan

Proceedings Volume 7649, 764910 (2010) https://doi.org/10.1117/12.847808

KEYWORDS: Adaptive optics, Wave propagation, Failure analysis, Structural health monitoring, Finite element methods, Composites, Wave plates, Aluminum, Alternate lighting of surfaces, Mechanical engineering

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Proceedings Article | 9 April 2010 Paper

A physically based classification approach for identifying AE source mechanism

D. Rajendra, A. Esterline, M. Sundaresan

Proceedings Volume 7648, 76480Y (2010) https://doi.org/10.1117/12.847781

KEYWORDS: Acoustic emission, Sensors, Acousto-optics, Numerical simulations, Image classification, Computer simulations, Structural health monitoring, Acoustics, Neodymium, Electromagnetic coupling

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Proceedings Article | 10 April 2008 Paper

Evaluation of bonded piezoelectric AE sensor for structural health monitoring

M. Jacques, P. Desai, F. Salih, M. Sundaresan

Proceedings Volume 6935, 693514 (2008) https://doi.org/10.1117/12.776542

KEYWORDS: Sensors, Acoustic emission, Wave propagation, Ferroelectric materials, Structural health monitoring, Wave plates, Transducers, Aluminum, Calibration, Interferometers

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Proceedings Article | 10 April 2007 Paper

Structural health monitoring in fuselage lap joint

G. Grandhi, F. Nkrumah, M. Jacques, M. Sundaresan

Proceedings Volume 6530, 65300K (2007) https://doi.org/10.1117/12.715906

KEYWORDS: Sensors, Acoustic emission, Structural health monitoring, Amplifiers, Ferroelectric materials, Visualization, Interferometers, Aluminum, Image segmentation, Nondestructive evaluation

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Proceedings Article | 10 April 2007 Paper

Multiple damage identification on a wind turbine blade using a structural neural system

Goutham Kirikera, Mark Schulz, Mannur Sundaresan

Proceedings Volume 6530, 65300T (2007) https://doi.org/10.1117/12.716071

KEYWORDS: Sensors, Structural health monitoring, Composites, Algorithm development, Data acquisition, Wave propagation, Actuators, Acoustic emission, Analog electronics, Wind turbine technology

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A large number of sensors are required to perform real-time structural health monitoring (SHM) to detect acoustic emissions (AE) produced by damage growth on large complicated structures. This requires a large number of high sampling rate data acquisition channels to analyze high frequency signals. To overcome the cost and complexity of having such a large data acquisition system, a structural neural system (SNS) was developed. The SNS reduces the required number of data acquisition channels and predicts the location of damage within a sensor grid. The sensor grid uses interconnected sensor nodes to form continuous sensors. The combination of continuous sensors and the biomimetic parallel processing of the SNS tremendously reduce the complexity of SHM. A wave simulation algorithm (WSA) was developed to understand the flexural wave propagation in composite structures and to utilize the code for developing the SNS. Simulation of AE responses in a plate and comparison with experimental results are shown in the paper. The SNS was recently tested by a team of researchers from University of Cincinnati and North Carolina A&T State University during a quasi-static proof test of a 9 meter long wind turbine blade at the National Renewable Energy Laboratory (NREL) test facility in Golden, Colorado. Twelve piezoelectric sensor nodes were used to form four continuous sensors to monitor the condition of the blade during the test. The four continuous sensors are used as inputs to the SNS. There are only two analog output channels of the SNS, and these signals are digitized and analyzed in a computer to detect damage. In the test of the wind turbine blade, multiple damages were identified and later verified by sectioning of the blade. The results of damage identification using the SNS during this proof test will be shown in this paper. Overall, the SNS is very sensitive and can detect damage on complex structures with ribs, joints, and different materials, and the system relatively inexpensive and simple to implement on large structures.

Proceedings Article | 17 March 2006 Paper

Study on actuating mode shapes of electro-active paper

Mannur Sundaresan, Yongkun Park, William Craft, Jag Sankar, Jaehwan Kim

Proceedings Volume 6168, 61680O (2006) https://doi.org/10.1117/12.658445

KEYWORDS: Actuators, Humidity, Molecules, Ions, Hydrogen, Dielectrics, Gold, Electrodes, Sodium, Electroactive polymers

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Proceedings Article | 15 March 2006 Paper

Real-time health monitoring of a thin composite beam using a passive structural neural system

G. Kirikera, V. Shinde, M. Schulz, A. Ghoshal, M. Sundaresan, J. Lee

Proceedings Volume 6177, 61770H (2006) https://doi.org/10.1117/12.658674

KEYWORDS: Sensors, Composites, Neurons, Data acquisition, Analog electronics, Wave propagation, Diagnostics, Structural health monitoring, Signal processing, Prototyping

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Proceedings Article | 19 May 2005 Paper

Verified models of multiagent systems for vehicle health management

Albert Esterline, Bhanu Gandluri, Mannur Sundaresan, Jagannathan Sankar

Proceedings Volume 5757, (2005) https://doi.org/10.1117/12.600017

KEYWORDS: Sensors, Systems modeling, Java, Neural networks, Prototyping, Control systems, Logic, Diagnostics, Computing systems, Fuzzy logic

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Proceedings Article | 19 May 2005 Paper

Identification of failure modes in composite materials

F. Nkrumah, G. Grandhi, M. Sundaresan, M. Derriso

Proceedings Volume 5767, (2005) https://doi.org/10.1117/12.600134

KEYWORDS: Sensors, Acoustic emission, Composites, Signal processing, Wavelets, Signal detection, Failure analysis, Digital signal processing, Wave sensors, Structural health monitoring

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Recently a new structural health monitoring system that employs a "continuous acoustic emission sensor" and an embeddable local processor has been proposed. The development of a processor that integrates the functions of signal conditioning, feature extraction, data storage, and digital communication is currently in progress. A prototype of this local processor chip has been developed. The integration of a continuous sensor with an embeddable local processor can potentially enable an inexpensive method of monitoring large and complex structures using acoustic emission signals. Such a system can reduce the cost, complexity, and weight of the required instrumentation. It is potentially scalable to large and complex structures and could be integrated into the structural material. The success of the acoustic emission based structural health monitoring technique depends on its ability to discriminate between valid acoustic emission signals and ambient noise. In addition, the technique should be able to identify the damage mode from the acoustic emission waveforms. This paper focuses on the use of acoustic emission technique for the identification of failure modes in composite materials. Three types of failure modes in glass fabric epoxy composite laminates are considered. These are two types of delamination growth and transverse crack growth. Wavelet analysis is used to extract time frequency information from the acoustic emission signals. Different features of the waveform including the frequency components, Symmetric and Antisymmetric components, and amplitudes are used to classify the signals and identify the failure modes. The laboratory tests indicate that it is possible to distinguish the individual failure modes under consideration. It was also possible to filter out spurious AE signals that originate from extraneous sources using an appropriate choice of sensors and frequency components. An attempt is made to relate the rate of damage growth with the detected acoustic emission signal parameters.

Proceedings Article | 17 May 2005 Paper

Monitoring fatigue crack growth in 7075 T6 aluminum using continuous sensor

G. Grandhi, F. Nkrumah, M. Sundaresan, J. Kemerling, D. Thomas

Proceedings Volume 5764, (2005) https://doi.org/10.1117/12.600165

KEYWORDS: Sensors, Acoustic emission, Aluminum, Interference (communication), Structural health monitoring, Signal detection, Signal processing, Wave plates, Digital filtering, Transducers

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Proceedings Article | 17 May 2005 Paper

Early detection of local buckling in structural members

Bashir Ali, Mannur Sundaresan, Mark Schulz, Derke Hughes

Proceedings Volume 5764, (2005) https://doi.org/10.1117/12.600152

KEYWORDS: Finite element methods, Composites, Structural health monitoring, Ferroelectric materials, Shape analysis, Wave propagation, Sensors, Safety, Reliability, Wind turbine technology

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Proceedings Article | 17 May 2005 Paper

Life prediction and life extension of composite specimens using acoustic emission technique

Francis Nkrumah, Mannur Sundaresan, Leonard Uitenham

Proceedings Volume 5764, (2005) https://doi.org/10.1117/12.600193

KEYWORDS: Acoustic emission, Composites, Failure analysis, Sensors, Ultrasonics, Structured optical fibers, Glasses, Analytical research, Nondestructive evaluation, Structural health monitoring

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Proceedings Article | 16 May 2005 Paper

Testing the analog processor of a structural neural system

G. Kirikera, I. Kang, J. Lee, V. Shinde, B. Westheider, Vesselin Shanov, M. Schulz, M. Sundaresan, A. Ghoshal

Proceedings Volume 5763, (2005) https://doi.org/10.1117/12.600243

KEYWORDS: Sensors, Neurons, Composites, Data acquisition, Analog electronics, Carbon nanotubes, Acoustic emission, Signal processing, MATLAB, Wave propagation

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Proceedings Article | 27 July 2004 Paper

Mimicking the biological neural system using electronic logic circuits

Goutham Kirikera, Vishal Shinde, Inpil Kang, Mark Schulz, Vesselin Shanov, Saurabh Datta, Doug Hurd, Bo Westheider, Mannur Sundaresan, Anindya Ghoshal

Proceedings Volume 5384, (2004) https://doi.org/10.1117/12.540174

KEYWORDS: Sensors, Aluminum, Acoustic emission, Ferroelectric materials, Analog electronics, Data acquisition, Neurons, Electronic circuits, Composites, Structural health monitoring

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Detecting and locating cracks in structural components and joints that have high feature densities is a challenging problem in the field of Structural Health Monitoring. There have been advances in piezoelectric sensors, actuators, wave propagation, MEMS, and optical fiber sensors. However, few sensor-signal processing techniques have been applied to the monitoring of joints and complex structural geometries. This is in part because maintaining and analyzing a large amount of data obtained from a large number of sensors that may be needed to monitor joints for cracks is difficult. Reliable low cost assessment of the health of structures is crucial to maintain operational availability and productivity, reduce maintenance cost, and prevent catastrophic failure of large structures such as wind turbines, aircraft, and civil infrastructure. Recently, there have also been advances in development of simple passive techniques for health monitoring including a technique based on mimicking the biological neural system using electronic logic circuits. This technique aids in reducing the required number of data acquisition channels by a factor of ten or more and is able to predict the location of a crack within a rectangular grid or within an arbitrarily arranged network of continuous sensors or neurons. The current paper shows results obtained by implementing this method on an aluminum plate and joint. The plates were tested using simulated acoustic emissions and also loading via an MTS machine. The testing indicates that the neural system can monitor complex joints and detect acoustic emissions due to propagating cracks. High sensitivity of the neural system is needed, and further sensor development and testing on different types of joints is required. Also indicated is that sensor geometry, sensor location, signal filtering, and logic parameters of the neural system will be specific to the particular type of joint (material, thickness, geometry) being monitored. Also, a novel piezoresistive carbon nanotube nerve crack sensor is presented that can become a neuron and respond to local crack growth.

Proceedings Article | 14 October 2003 Paper

Active fiber composite continuous sensors

Saurabh Datta, Goutham Kirikera, Mark Schulz, Mannur Sundaresan

Proceedings Volume 5062, (2003) https://doi.org/10.1117/12.514679

KEYWORDS: Sensors, Composites, Atrial fibrillation, Fiber optics sensors, Acoustic emission, Electrodes, Manufacturing, Structured optical fibers, Active sensors, Ferroelectric materials

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Proceedings Article | 1 August 2003 Paper

Continuous sensors for structural health monitoring

Saurabh Datta, Goutham Kirikera, Mark Schulz, Mannur Sundaresan

Proceedings Volume 5046, (2003) https://doi.org/10.1117/12.484120

KEYWORDS: Sensors, Composites, Atrial fibrillation, Fiber optics sensors, Acoustic emission, Electrodes, Data acquisition, Active sensors, Manufacturing, Ferroelectric materials

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Proceedings Article | 1 August 2003 Paper

Recent advances in an artificial neural system for structural health monitoring

Goutham Kirikera, Saurabh Datta, Mark Schulz, Anindya Ghoshal, Mannur Sundaresan, Jeff Feaster, Derke Hughes

Proceedings Volume 5046, (2003) https://doi.org/10.1117/12.484118

KEYWORDS: Sensors, Neurons, Wave propagation, Structural health monitoring, Acoustic emission, Ferroelectric materials, Systems modeling, Composites, Glasses, Nerve

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Proceedings Article | 14 November 2002 Paper

Embedded continuous sensor for monitoring damage evolution in composite materials

Mannur Sundaresan, Gangadhararao Grandhi, Mark Schulz, Goutham Kirikera

Proceedings Volume 4935, (2002) https://doi.org/10.1117/12.476119

KEYWORDS: Sensors, Neurons, Acoustic emission, Signal processing, Composites, Signal detection, Biomimetics, Structural health monitoring, Digital signal processing, Structural design

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Proceedings Article | 14 November 2002 Paper

Early detection of local buckling in composite bars

Mannur Sundaresan, Bashir Ali, Frederick Ferguson, Mark Schulz

Proceedings Volume 4935, (2002) https://doi.org/10.1117/12.476126

KEYWORDS: Composites, Ferroelectric materials, Structural health monitoring, Sensors, Actuators, Transmitters, Receivers, Mechanical engineering, Smart structures, Wind turbine technology

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Proceedings Article | 11 June 2002 Paper

Piesoceramic and nanotube materials for health monitoring

Mark Schulz, Goutham Kirikera, Saurabh Datta, Mannur Sundaresan

Proceedings Volume 4702, (2002) https://doi.org/10.1117/12.469883

KEYWORDS: Sensors, Neurons, Composites, Carbon nanotubes, Carbon, Boron, Actuators, Signal processing, Structural health monitoring, Atrial fibrillation

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Proceedings Article | 11 June 2002 Paper

Artificial nerve system for structural monitoring

William Martin, Anindya Ghoshal, Mannur Sundaresan, Gary Lebby, Mark Schulz, Promod Pratap

Proceedings Volume 4702, (2002) https://doi.org/10.1117/12.469909

KEYWORDS: Sensors, Ferroelectric materials, Structural health monitoring, Data acquisition, Receptors, Semiconducting wafers, Acoustic emission, Resistance, Capacitance, Composites

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Proceedings Article | 6 August 2001 Paper

Neural system for structural health monitoring

Mannur Sundaresan, Mark Schulz, Anindya Ghoshal, William Martin, Promod Pratap

Proceedings Volume 4328, (2001) https://doi.org/10.1117/12.435516

KEYWORDS: Sensors, Neurons, Signal processing, Data processing, Composites, Axons, Structural health monitoring, Action potentials, Dendrites, Data communications

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Proceedings Article | 14 June 2000 Paper

Active fiber composites for structural health monitoring

Mark Schulz, Mannur Sundaresan, Anindya Ghoshal, Perngjin Pai

Proceedings Volume 3992, (2000) https://doi.org/10.1117/12.388196

KEYWORDS: Sensors, Composites, Wave propagation, Electrodes, Structural health monitoring, Acoustic emission, Ferroelectric materials, Nondestructive evaluation, Acoustics, Dielectrics

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