Adaptive neuro-fuzzy inference system in modelling fatigue life of multidirectional composite laminates

Adaptive neuro-fuzzy inference system (ANFIS) has been successfully used for the modelling of fatigue behaviour of a multidirectional composite laminate. The evaluation of the neuro-fuzzy model has been performed using a data base containing 257 valid fatigue data points. Coupons were cut at 0 degrees on-axis and 15 degrees, 30 degrees,45 degrees, 60 degrees, 75 degrees, and 90 degrees off-axis directions from an E-glass/polyester multidirectional laminate with a stacking sequence of [O/(+/- 45)(2)/O](T). Constant amplitude fatigue tests at different tensile and compressive conditions were conducted for the determination of the 17 S-N curves. The modelling accuracy of this novel, in this field, computational technique is very high. For all cases studied, it has been proved that a portion of around 50% of the available data are adequate for accurate modelling of the fatigue behaviour of the material under consideration. The new technique is a stochastic process which leads to the derivation of a multi-slope S-N curve based on the available experimental data without the need for any assumptions. Employment of this technique can lead to a substantial decrease of the experimental cost for the determination of reliable fatigue design allowables. (C) 2008 Elsevier B.V. All rights reserved.

Published in:
Computational Materials Science, 43, 1086-1093

 Record created 2010-11-30, last modified 2018-03-17

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