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- W2287519500 abstract "According to (Culshaw, 1996), a smart structure can be defined as one that monitors itself and/or its environment in order to respond to changes in its condition. Fibre Bragg grating sensors (FBG) can be very useful in applications where layered materials, such as composites, are involved. Due to the fact that fibre optic sensors are small, multiplexable, electrically isolated and immune to electromagnetic fields, they can give engineers the possibility to incorporate a fibre optic nervous systems into their composite material designs. These sensors allow measurement of parameters such as load/strain, vibration, temperature and detection of cracks and delamination phenomena (Culshaw, 1996). When FBGs are applied to the measurement of strain their cross-sensitivity to temperature is an issue that needs to be addressed. One approach to solve this problem it is to design sensing heads insensitive to temperature. Another one is the conception of structures with sufficient degrees of freedom to permit the simultaneous discrimination of these two parameters. However, when the sensing head contains two devices, the error increases due to the cross component of the physical parameter. Usually, the matrix method to solve the problem is acceptable, but when the instability of the matrix is high, it is necessary to apply other alternative solutions. Other disadvantage of the matrix method is when the response of the sensors is non linear. This problem appears when the sensors are characterized in temperature or embedded in laminated composites. In this work the authors present an alternative solution using an artificial neural approach to reduce the errors obtained by the matrix method in optical fibre Bragg grating embedded in laminated composite. The optical sensor presents linear response for strain and non linear response for temperature. 31" @default.
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- W2287519500 date "2008-05-24" @default.
- W2287519500 modified "2023-09-27" @default.
- W2287519500 title "Intelligent Optical Sensors Using Artificial Neural Network Approach" @default.
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- W2287519500 doi "https://doi.org/10.1007/978-0-387-09492-2_31" @default.
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