On the Impact of Manufacturing Uncertainty in Structural Health Monitoring of Composite Structures: A Signal to Noise Weighted Neural Network ProcessReportar como inadecuado




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This article investigates the potential impact of manufacturing uncertainty in composite structures here in the form of thickness variation in laminate plies, on the robustness of commonly used Artificial Neural Networks ANN in Structural Health Monitoring SHM. Namely, the robustness of an ANN SHM system is assessed through an airfoil case study based on the sensitivity of delamination location and size predictions, when the ANN is imposed to noisy input. In light of the observed poor performance of the original network, even when its architecture was carefully optimized, it had been proposed to weigh the input layer of the ANN by a set of signal-to-noise SN ratios and then trained the network. Both damage location and size predictions of the latter SHM approach were increased to above 90%. Practical aspects of the proposed robust SN-ANN SHM have also been discussed.

KEYWORDS

Composite Structures, Manufacturing Uncertainties, Structural Health Monitoring, Artificial Neural Networks, Signal-to-Noise Weighting

Cite this paper

Teimouri, H. , Milani, A. , Seethaler, R. and Heidarzadeh, A. 2016 On the Impact of Manufacturing Uncertainty in Structural Health Monitoring of Composite Structures: A Signal to Noise Weighted Neural Network Process. Open Journal of Composite Materials, 6, 28-39. doi: 10.4236-ojcm.2016.61004.





Autor: Hessamodin Teimouri1, Abbas S. Milani1, Rudolf Seethaler1, Amir Heidarzadeh2

Fuente: http://www.scirp.org/



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