Ultimate Strength Prediction of Carbon/Epoxy Tensile Specimens from Acoustic Emission Data

Abstract

Abstract:

The objective of this paper was to predict the residual strength of post impacted carbon/epoxy composite laminates using an online acoustic emission (AE) monitoring and artificial neural networks (ANN). The laminates were made from eight-layered carbon (in woven mat form) with epoxy as the binding medium by hand lay-up technique and cured at a pressure of 100 kg/cm² under room temperature using a 30 ton capacity compression molding machine for 24 h. 21 tensile specimens (ASTM D3039 standard) were cut from the cross ply laminates. 16 specimens were subjected to impact load from three different heights using a Fractovis Plus drop impact tester. Both impacted and non-impacted specimens were subjected to uniaxial tension under the acoustic emission monitoring using a 100 kN FIE servo hydraulic universal testing machine. The dominant
AE parameters such as counts, energy, duration, rise time and amplitude are recorded during monitoring. Cumulative counts corresponding to the amplitude ranges obtained during the tensile testing are used to train the network. This network can be used to predict the failure load of a similar specimen subjected to uniaxial tension under acoustic emission monitoring for certain percentage of the average failure load.

Key words: Acoustic emission (AE), Carbon/epoxy laminate, Tensile testing, Artificial neural networks

V. Arumugamy R. Naren Shankar B.T.N. Sridhar A. Joseph Stanley. Ultimate Strength Prediction of Carbon/Epoxy Tensile Specimens from Acoustic Emission Data[J]. J Mater Sci Technol, 2010, 26(8): 725-729.

References

[1 ] J.L.Walker II: Ultimate Strength Prediction of ASTM D 3039 Tensile Specimens from Acoustic Emission Amplitude Data, AIAA 92-0258. [2 ] J.L. Walker, G.L. Workman and S.S. Russell: An Acoustic Emission and Acousto-ultrasonic Analysis of Impact Damaged Composite Pressure Vessels, 1996, May. [3 ] P. Werbos: Proc. IEEE, 1990, 78(10), 1550. [4 ] G.E. Husman, J.M. Whitney and J.C. Halpin: Residual Strength Characterization of Laminated Composites Subjected to Impact Loading, in Foreign Object Impact Damage to Composites, ASTM STP 568, American Society tor Testing and Materials, Philadelphia, 1975, 92. [5 ] A.T. Nettles: Instrumented Impact and Residual Tensile Strength Testing of Eight-Ply Carbon/Epoxy Specimens, in NASA Technical Paper 2981, 1990. [6 ] M. Akav: Compos. Sci. Technol., 1988, 33, 1. [7 ] B. Lloyd and G. Knight: Impact Damage Sensitivity of Filament-Wound Composite Pressure Vessels, Vol. 1, in The 1986 JANNAF Propulsion Meeting, Johns Hopkins Univ., 1986, 7. [8 ] C. Soutis and P.T.Curtis: Compos. Sci. Technol., 1996, 56, 677. [9 ] D.R. Moore and R.S. Prediger: Polym. Composite., 1988, 9, 330. [10] A.T. Nettles: Instrumented Impact and Residual Tensile Strength Testing of Eight-Ply Carbon/Epoxy Specimens, in NASA Technical Paper 2981, 1990. [11] J. Rodgers: J. Acous. Emiss., 1985, 4, 1. [12] J. Cohen and J. Awerbuch: Monitoring Delamination Progression in Composites through Acoustic Emission During Fatigue Loading, in Proc. of Japan-U.S. Conference on Composite Materials, Washington, D.C., 1988, 1035. [13] N.C. Shippenand D.F. Adam: J. Reinf. Plast. Compos., 1985, 4, 242. [14] G. Caprino, R. Teti and I. de Iorio: Compos. Part B-Eng., 2005, 36, 365. [15] H.N. Bar, M.R. Bhat and C.R.L. Murthy: Compos. Struct., 2004, 65, 231. [16] R. Dua, S.E. Watkins, D.C. Wunsch, K. Chandrashekhara and F. Akhavan: Detection and Classification of Impact-Induced Damage in Composite Plates using Neural Networks, in INNS-IEEE Int. Joint Conf. on Neural Networks, Mount Royal, NJ, International Neural Network Society, Washington, DC, July 2001, 51.

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