KEYWORDS: Composites, Sensors, Feature extraction, Machine learning, Waveguides, Data modeling, Principal component analysis, Structural health monitoring, Statistical analysis, Thin films
We propose a model assisted method to identify damage types and severity based on mode converted wave strength. Machine learning techniques are employed to develop classification models complemented by the finite element simulation models. Finite element simulation models provide the training data for various cases of damage and severity involving common types of damages in composites. Damage classification models are based on mode conversion strength versus frequency curves of participating four wave modes. For damage recognition and classification, a multi-layer Convoluted Neural Network (CNN) has been trained using the back-propagation paradigm on the generated dataset.
Wind turbines and aircraft structures require smooth surfaces due to the aerodynamic requirement. The surface mounted transducers for structural health monitoring disturbs the airflow over the surface. Embedding the sensors in the structures is a viable solution, which also leads to the self-protection and shielding of the sensors. A brief review of techniques to embed the sensors and their wiring is provided. Materials like piezo-polymers, nanocomposites, macro-fiber composites and ceramics have been investigated for the compatibility with composite laminate. The atomistic-continuum, micromechanics and finite element based approaches used to derive the effective properties of nanocomposites have been discussed. With the capability to change the density and stiffness of nanocomposites, the possibilities of very high compatibility with the host structure are explored. The relation of compatibility with the material parameters like density, axial stiffness, coupling stiffness and flexural stiffness has been discussed. We present the effect of embedding thin film sensors on the dynamic response of a composite laminated beam using a spectral finite element model. The change in stiffness and natural frequencies have been quantified. The study provides a strong base to design embedded sensing technology for advanced structures made of composite laminates and sandwich sections.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.