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Detection of Fibre Waviness Using Ultrasonic Array Scattering Data

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Abstract

Composite materials owe their success to the ability to favour mechanical properties in specific directions whilst minimising the weight of components. Although the composite manufacturing process has been progressively improved, subtle defects such as fibre waviness are still commonplace. Any localised departure of a ply from the desired lay-up direction is known to adversely affect strength. Therefore, manufacturers and end users are interested in detecting defects such as fibre waviness at various stages during prototyping and as part of the manufacturing process.

In this paper, an ultrasonic array is used to both image the composite and extract information that characterises the scattering of the interior structure. The scattering information is encoded in the scattering matrix: defined as the far field amplitude of scattered signals from a defect as a function of the incident and scattering angles. A method for extracting the scattering matrix from experimental array data over a spatially localised region is presented. Ultimately this could lead to the ability to map the distribution of scattering behaviour within the composite. The method is demonstrated on composite samples containing various levels of waviness. It is also shown that use of the differences in the scattering matrices can offer the possibility to statistically differentiate wavinesses of different nature and severity.

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Acknowledgements

The work was funded through the Engineering and Physical Sciences Research Council (EPSRC) and Rolls-Royce plc. The authors would like to thanks Tim Barden from Rolls-Royce plc for his advice and providing some of the samples.

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Authors and Affiliations

  1. Department of Mechanical Engineering, University of Bristol, Bristol, BS8 1TR, UK

    Damien Pain & Bruce W. Drinkwater

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  1. Damien Pain
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  2. Bruce W. Drinkwater
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Correspondence to Damien Pain.

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Pain, D., Drinkwater, B.W. Detection of Fibre Waviness Using Ultrasonic Array Scattering Data. J Nondestruct Eval 32, 215–227 (2013). https://doi.org/10.1007/s10921-013-0174-z

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  • DOI: https://doi.org/10.1007/s10921-013-0174-z

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