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• W3122180419 abstract "Laser-Ultrasonics Testing (LUT) is a Non-Destructive Technique (NDT) with good potential for application in the railway sector, nevertheless this technique is not yet used in practice because there are some practical difficulties to overcome. The possibility of measuring on a complete wheelset by bypassing the keying of the wheel will allow drastically reducing the inspection time but it has not yet been demonstrated. In fact, the attenuation of the signal in the path makes complex the interpretation of the generated waves. This paper aims at illustrating how the combination of simulated and experimental data allows to optimize the test setup for having output data of unambiguous interpretation. The main innovations presented in this paper are: (i) the possibility to work with low energy waves in the thermoelastic-ablative limit while maintaining satisfactory contrast levels for the purpose of defect detection and (ii) the implementation of a complete Finite Element Model (FEM) including the generation and propagation of waves in the solid domain and the propagation in air. This last step has not considered before in previous papers. The model allows to define the optimal experimental conditions to have a measured signal with an adequate signal-to-noise ratio (SNR > 6 dB) and to define an experimental procedure for defect detection reliable and comparable with current standards. This study lays the foundations for an innovative approach for train axle diagnostics which can be used during train extraordinary maintenance interventions. The laser ultrasonics system presented in this paper can be likely integrated in the pit lathe and exploited to monitor the railway wheels during their re-profiling phase, without having to remove them from the vehicle." @default.
• W3122180419 created "2021-02-01" @default.
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• W3122180419 date "2021-05-01" @default.
• W3122180419 modified "2023-10-14" @default.
• W3122180419 title "FEM based design of experiment for train wheelset diagnostics by laser ultrasonics" @default.
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• W3122180419 doi "https://doi.org/10.1016/j.ultras.2021.106368" @default.
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