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• W2767183766 endingPage "155014771774370" @default.
• W2767183766 startingPage "155014771774370" @default.
• W2767183766 abstract "Aircraft structural damage detection is becoming of increased importance. Technologies such as acousto-ultrasonic have been suggested for this application; however, an optimization strategy for sensor network design is required to ensure a high detection probability while minimizing sensor network mass. A methodology for optimizing acousto-ultrasonic transducer placement for adhesive disbond detection on metallic aerospace structures is presented. Experimental data sets were acquired using three-dimensional scanning laser vibrometry enabling in-plane and out-of-plane Lamb wave components to be considered. This approach employs a novel multi-sensor site strategy which is difficult to achieve with physical transducers. Different excitation frequencies and source–damage–sensor paths were considered. A fitness assessment criterion which compared baseline and damaged data sets using cross-correlation coefficients was developed empirically. Efficient sensor network optimization was achieved using a bespoke genetic algorithm for different network sizes with the effectiveness assessed and discussed. A comparable numerical data set was also produced using the local interaction simulation approach and optimized using the same methodology. Comparable results with those of the experimental data set indicated a good agreement. As such, the numerical approach demonstrates that acousto-ultrasonic sensor networks can be optimized using simulation (with some further refinement) during an aircraft design phase, being a useful tool to sensor network designers." @default.
• W2767183766 created "2017-11-10" @default.
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• W2767183766 date "2017-11-01" @default.
• W2767183766 modified "2023-10-18" @default.
• W2767183766 title "Optimization of acousto-ultrasonic sensor networks using genetic algorithms based on experimental and numerical data sets" @default.
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• W2767183766 doi "https://doi.org/10.1177/1550147717743702" @default.
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