Impact Identification in Composite Stiffened Panels

Abstract:

A number of small mass and large mass impacts on a sensorised aircraft stiffened panel were numerically simulated. Sensor signals and the contact force history were recorded during each impact. A significant difference was noticed between the small mass and large mass impacts with respect to the contact force. To distinguish between these two types of impacts, the Fast Fourier Transform was performed on the sensor signals and a categorisation criterion was defined. Finally, two separate Artificial Neural Networks were trained to approximate the peak contact force for each type of impact. It was found that the performance of these ANNs were better than a single ANN trained for both small and large mass impacts.

• Periodical: Key Engineering Materials (Volumes 525-526)
• Pages: 565-568
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.525-526.565
• Online since: November 2012
• Authors: M. Ghajari, Z. Sharif-Khodaei, Ferri M.H.Aliabadi
• Keywords: Artificial Neural Network (ANN), Composite, Impact, Large Mass, Small Mass, Stiffened Panel

References

[1] Z. Sharif Khodaei and M. Aliabadi, Damage Identification Using Lamb Waves, Key Engineering Materials, vol. 452, pp.29-32, (2011).

DOI: 10.4028/www.scientific.net/kem.452-453.29

[2] I. Benedetti, M. Aliabadi, and A. Milazzo, A fast BEM for the analysis of damaged structures with bonded piezoelectric sensors, Computer Methods in Applied Mechanics and Engineering, vol. 199, pp.490-501, (2010).

DOI: 10.1016/j.cma.2009.09.007

[3] Z. Sharif Khodaei, R. Rojas-Diaz, and M. Aliabadi, Lamb-Wave Based Technique for Impact Damage Detection in Composite Stiffened Panels, Key Engineering Materials, vol. 488, pp.5-8, (2012).

DOI: 10.4028/www.scientific.net/kem.488-489.5

[4] J. Park, S. Ha, and F. K. Chang, Monitoring Impact Events Using a System-Identification Method, AIAA journal, vol. 47, pp.2011-2020, (2009).

[5] M. T. Martin and J. F. Doyle, Impact force identification from wave propagation responses, International Journal of Impact Engineering, vol. 18, pp.65-77, (1996).

DOI: 10.1016/0734-743x(95)00022-4

[6] K. Worden and W. J. Staszewski, Impact location and quantification on a composite panel using neural networks and a genetic algorithm, Strain, vol. 36, pp.61-68, (2000).

DOI: 10.1111/j.1475-1305.2000.tb01175.x

[7] W. J. Staszewski, K. Worden, R. Wardle, and G. R. Tomlinson, Fail-safe sensor distributions for impact detection in composite materials, Smart Materials and Structures, vol. 9, p.298, (2000).

DOI: 10.1088/0964-1726/9/3/308

[8] S. Dae-Un, O. Jung-Hoon, K. Chun-Gon, and H. Chang-Sun, Impact monitoring of smart composite laminates using neural network and wavelet analysis, Journal of intelligent material systems and structures, vol. 11, pp.180-190, (2000).

DOI: 10.1106/n5e7-m37y-3mar-2kfh

[9] J. Haywood, P. T. Coverley, W. J. Staszewski, and K. Worden, An automatic impact monitor for a composite panel employing smart sensor technology, Smart Materials and Structures, vol. 14, p.265, (2005).

DOI: 10.1088/0964-1726/14/1/027

[10] M. Ghajari, Z. Sharif Khodaei, and M. Aliabadi, Impact Detection Using Artificial Neural Networks, Key Engineering Materials, vol. 488, pp.767-770, (2012).

DOI: 10.4028/www.scientific.net/kem.488-489.767

[11] R. Olsson, Mass criterion for wave controlled impact response of composite plates, Composites Part A: Applied Science and Manufacturing, vol. 31, pp.879-887, (2000).

DOI: 10.1016/s1359-835x(00)00020-8