Paper
16 April 1998 Embedded piezoceramics in composites for damage sensing and mitigation: design issues
P. Hajela, Y. Teboub
Author Affiliations +
Proceedings Volume 3321, 1996 Symposium on Smart Materials, Structures, and MEMS; (1998) https://doi.org/10.1117/12.305544
Event: Smart Materials, Structures and MEMS, 1996, Bangalore, India
Abstract
The use of embedded piezoceramic elements in composite structures provides a simultaneous sensing and actuation capability that has applications in the problem of damage sensing and mitigation. The present paper describes an approach wherein strain readings from embedded sensors can be used to determine the state of the structure. The sensor elements can also function as actuators, and once a loss in structural integrity is established, compensatory strains can be induced in the embedded elements through actuation to control the growth of damage by redistribution of loads around the critical region. Since real-time response is critical for both damage sensing and mitigation, the approach studied is one based on using trained neural networks to establish the desired functional relations. Both the sensing operation and any required actuation is shown to benefit from an optimal placement of the piezoceramic elements in the structure. This design problem is formulated as a nonlinear optimization problem, which includes a mix of continuous, integer, and discrete design variables. A genetic algorithm based optimization strategy is used, where computational expediency requires the use of global function approximations. The approach is implemented in the design of composite beam structures with delamination.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
P. Hajela and Y. Teboub "Embedded piezoceramics in composites for damage sensing and mitigation: design issues", Proc. SPIE 3321, 1996 Symposium on Smart Materials, Structures, and MEMS, (16 April 1998); https://doi.org/10.1117/12.305544
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Cited by 1 scholarly publication.
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KEYWORDS
Sensors

Neurons

Composites

Neural networks

Actuators

Genetic algorithms

Gallium

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