Abstract
A series of 18 tensile coupons were monitored with an acoustic emission (AE) system, while loading them up to failure. AE signals emitted due to different failure modes in tensile coupons were recorded. Amplitude, duration, energy, counts, etc., are the effective parameters to classify the different failure modes in composites, viz., matrix crazing, fiber cut, and delamination, with several subcategories such as matrix splitting, fiber/matrix debonding, fiber pullout, etc. Back propagation neural network was generated to predict the failure load of tensile specimens. Three different networks were developed with the amplitude distribution data of AE collected up to 30%, 40%, and 50% of the failure loads, respectively. Amplitude frequencies of 12 specimens in the training set and the corresponding failure loads were used to train the network. Only amplitude frequencies of six remaining specimens were given as input to get the output failure load from the trained network. The results of three independent networks were compared, and we found that the network trained with more data was having better prediction performance.
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Ativitavas N, Pothisiri T, Fowler TJ (2006) Identification of fiber reinforced plastic failure mechanisms from acoustic emission data using neural networks. J Compos Mater 40(3):193–226
Hill EVK, Walker JL, Rowell GH (1996) Burst pressure prediction in graphite/epoxy pressure vessels using neural networks and acoustic emission amplitude data. Mater Eval 54(6):744–748
Fisher ME, Hill EK (1998) Neural network burst pressure prediction in fiber glass epoxy pressure vessels using acoustic emission. Mater Eval 56(2):1395–1401
Miller RK, McIntire P (1987) Nondestructive testing handbook, acoustic emission, vol. 5. 2nd edn. ASNT, Columbus
Chelladurai T, Krishnamurthy R, Acharya AR (1989) An approach for the integrity assessment of M250 maraging steel pressurized systems. J Acoust Emiss 8(1–2):88–92
Pollock AA (1981) Acoustic emission amplitude distributions. Int Adv Nondestr Test 7:215–239
Walker JL, Hill EvK (1992) Amplitude distribution modeling and ultimate strength prediction of ASTM D-3039 graphite/epoxy tensile specimens. Proceeding from the fourth International symposium on Acoustic Emission from composite materials.(AECM-4). The American Society for Nondestructive Testing, Columbus, pp 115–131
Sivanandam SN, Sumathi S, Deepa SN (2005) Introductions to neural networks using MATLAB 6.0. Tata McGraw-Hill, New Delhi, ISBN-13:978-0-07-059112-7
Walker JL, Hill EvK (1996) Back propagation neural network for predicting ultimate strengths of unidirectional graphite/epoxy tensile specimens. Adv Perform Mater 3(1):75–83
Hill EVK, Israel PL, Knotts GL (1993) Neural network prediction of aluminum–lithium weld strength from acoustic emission amplitude data. Mater Eval 66(51):1040–1045
Prosser WH, Jackson KE, Kellas S, Smith BT (1995) Advanced waveform based acoustic emission detection of matrix cracking in composites. Mater Eval 53(9):1052–1058
Ely TM, Hil EVK (1995) Longitudinal splitting and fiber breakage characterization in graphite epoxy using acoustic emission data. Mater Eval 53(2):288–294
Hubele NF, Hwarng HB (1994) A neural network model and multiple linear regression: Another point of view. In: Dagli CH, Fernandez BR, Ghosh J, Kumara RTS (eds) Intelligent engineering systems through artificial neural networks. vol. 4. ASME, NewYork, pp 199–203
Fausett LV (1994) Fundamentals of neural networks: Architectures, algorithms and applications. Prentice Hall, Englewood Cliffs, pp 328–330
Kalloo and Frederick.R Predicting burst pressure in filament wound composite vessels using acoustic emission data. M.S Thesis, Embry-Riddle Aeronautical University, 1988
Fatzinger EC, Hill EVK (2005) Low proof load prediction of ultimate loads of fiber glass/epoxy resin I-beams using acoustic emission. J Test Eval 33(5):340–347
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Open Access This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License (https://creativecommons.org/licenses/by-nc/2.0), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
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Rajendraboopathy, S., Sasikumar, T., Usha, K.M. et al. Artificial neural network a tool for predicting failure strength of composite tensile coupons using acoustic emission technique. Int J Adv Manuf Technol 44, 399–404 (2009). https://doi.org/10.1007/s00170-008-1874-x
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DOI: https://doi.org/10.1007/s00170-008-1874-x