NEURAL NETWORKS AND EVOLUTIONARY OPTIMIZATION TECHNIQUES AND THEIR APPLICATIONS IN FATIGUE LIFE ASSESSMENT OF COMPOSITE MATERIALS-A BRIEF REVIEW

Mas Irfan P. Hidayat, Victor D. Waas, Rezza Ruzuqi

Abstract


Modeling of fatigue life of composite materials under various loading and environment conditions becomes important and challenging task from viewpoint of performance and reliability as it forms a basis for lifetime assessment of composite structures under complex variable state of stress. Application of soft computing techniques as new approach and route for modelling of composite material fatigue lives has attracted a great interest recently. The applications of soft computing techniques in fatigue life assessment of composite materials are reviewed and discussed in this paper.

Keywords


Neural networks; evolutionary algorithms; optimization; fatigue life assessment; composite materials

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References


Al-Assaf, Y. and El-Kadi, H. (2001). Fatigue life prediction of unidirectional glass fiber/epoxy composite laminae using neural networks. Composites Structures, 53(6), 65-71.

Aymerich, F. and Serra, M. (1998). Prediction of fatigue strength of composite laminates by means of neural networks. The Third Seminar on Experimental Techniques and Design in Composite Materials, 30-31 October 1996, Cagliari, Italy, pp. 231-240. DOI: 10.4028/www.scientific.net/KEM.144.231.

Azar, A.T. (2013). Fast neural network learning algorithms for medical applications. Neural Computing and Applications, 23(3-4), 1019–1034.

Bucar, T., Nagode, M. and Fajdiga, M. (2007). An improved neural computing method for describing the scatter of S–N curves. International Journal of Fatigue, 29(12), 2125-2137.

Bukkapatnam, S.T.S. and Sadananda, K. (2005). A genetic algorithm for unified approach-based predictive modeling of fatigue crack growth. International Journal of Fatigue, 27(10-12), 1354-1359.

Eeckman, F. H. (1992). Analysis and Modeling of Neural Systems. Boston: Kluwer Academic.

El-Kadi, H. and Al-Assaf, Y. (2002). Prediction of the fatigue life of unidirectional glass fiber/epoxy composite laminae using different neural network paradigms. Composites Structures, 55(1), 239-246.

Fathi, A. and Aghakouchak, A.A. (2007). Prediction of fatigue crack growth rate in welded tubular joints using neural network. International Journal of Fatigue, 29(2), 261-275.

Fletcher, R. (1980). Practical Methods of Optimization. USA: John Wiley & Sons, Ltd.

Fogel, L.J., Owens, A.J. and Walsh, M.J. (1966). Artificial Intelligence Through Simulated Evolution. USA: Wiley.

Funahashi, K.I. (1989). On the approximate realization of continuous mappings by neural networks. Neural Networks, 2(3), 183–192.

Foresee, F.D. and Hagan, M.T. (1997). Gauss-Newton approximation to bayesian learning. The 1997 IEEE International Conference on Neural Networks (ICNN), 9-12 June 1997, Houston, TX, USA, pp. 1930-1935. DOI: 10.1109/ICNN.1997.614194.

Fraser, N. (1998). The biological neuron. Retrieved May 1, 2015 from http://www.virtualventures.ca/~neil/neural/neuron-a.html.

Freire Junior, R.C.S., Neto, A.D.D. and de Aquino, E.M.F. (2005). Building of constant life diagrams of fatigue using artificial neural networks. International Journal of Fatigue, 27(7), 746-751.

Freire Junior, R.C.S., Neto, A.D.D. and de Aquino, E.M.F. (2007). Use of modular networks in the building of constant life diagrams. International Journal of Fatigue, 29(3), 389-396.

Freire Junior, R.C.S., Neto, A.D.D. and de Aquino, E.M.F. (2009). Comparative study between ANN models and conventional equations in the analysis of fatigue failure of GFRP. International Journal of Fatigue, 31(5), 831-839.

Grosan, C. and Abraham, A. (2007). Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews. Studies in Computational Intelligence, 75, 1–17.

Hartman, E.J., Keeler, J.D. and Kowalski, J. M. (1990). Layered neural networks with Gaussian hidden units as universal approximations. Neural Computation, 2(2), 210–215.

Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor, MI: The University of Michigan Press.

Haque, M.E. and Sudhakar, K.V. (2002). ANN back-propagation prediction model for fracture toughness in microalloy steel. International Journal of Fatigue, 24(9), 1003-1010.

Hornik, K., Stinchcombe, M., and White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359–366.

Hour, K.Y. & Sehitoglu, H. (1993). Proof testing of composite materials. Journal of Composite Materials, 27, 782-805.

Koza, J.R. (1992). Genetic Programming. Cambridge, MA: MIT Press.

Klemenc, J. and Fajdiga, M. (2012). Estimating S–N curves and their scatter using a differential antstigmergy algorithm. International Journal of Fatigue, 43(1), 90–97.

Klemenc, J. and Fajdiga, M. (2013). Joint estimation of E–N curves and their scatter using evolutionary algorithms. International Journal of Fatigue, 56(1), 42–53.

Lee, J.A. and Almond, D.P. (2003). A Neural-network approach to fatigue life prediction. In B. Harris (Ed.), Fatigue in Composites. Ch. 21. Cambridge: Woodhead Publishing.

MacKay, D.J.C., 2004. Information Theory, Inference and Learning Algorithms. England: Cambridge University Press.

Majidian, A. and Saidi, M.H. (2007). Comparison of fuzzy logic and neural network in life prediction of boiler tubes. International Journal of Fatigue, 29(3), 489-498.

Manson, S.S. and Halford, G.R. (2006). Fatigue and Durability of Structural Materials. USA: ASM International.

Mohamed, A.W., Sabry, H.Z. and Khorsid, M. (2012). An alternative differential evolution algorithm for global optimization. Journal of Advanced Research, 3, 149-165.

Nabney, I.T., 2002. NETLAB Algorithms for Pattern Recognition. London: Springer.

Passipoularidis, V.A. and Philippidis, T.P. (2009). Residual strength after fatigue in composites: theory vs experiment. International Journal of Fatigue, 29(12), 2104–2116.

Passipoularidis, V.A. and Philippidis, T.P. (2009). A study of factors affecting life prediction of composites under spectrum loading. International Journal of Fatigue, 31(3), 408–417.

Philippidis, T.P., Passipoularidis, V.A. and Brondsted, P. (2011). Fatigue life prediction in composites using progressive damage modelling under block and spectrum loading. International Journal of Fatigue, 33(2), 132–144.

Post, N.L., Case, S.W. and Lesko, J.J. (2008). Modeling the variable amplitude fatigue of composite materials: A review and evaluation of the state of the art for spectrum loading. International Journal of Fatigue, 30(12), 2064-2086.

Price, K., Storn, R. and Lampinen, J. (2005). Differential Evolution: A Practical Approach to Global Optimization. Berlin: Springer.

Pujol, J.C.F. and Pinto, J.M.A. (2011). A neural network approach to fatigue life prediction. International Journal of Fatigue, 33(3), 313-322.

Rechenberg, I. (1973). Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Stuttgart: Fromman-Holzboog.

Reifsnider, K.L. (Ed.). (1991). Fatigue of composite materials. Amsterdam: Elsevier.

Rumelhart, D.E., McClelland, J.L. and University of California–San Diego PDP Research Group (1986). Parallel Distributed Processing: Explorations in The Microstructure of Cognition. Cambridge: MIT Press.

Schijve, J. ed., 2009. Fatigue of Structures and Materials. Netherlands: Springer, pp. 373-394.

Sendeckyj, G.P. (1991). Life prediction for resin–matrix composite materials. In K.L. Reifsnider (Ed.), Fatigue of Composite Materials. Amsterdam: Elsevier.

Sendeckyj, G.P. (2001). Constant life diagrams — a historical review. International Journal of Fatigue, 23(4), 347-353.

Storn, R. and Price, K. (1995). Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012. ICSI.

Storn, R. and Price, K. (1997). Differential evolution-a simple heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341-359.

Subudhi, B. and Jena, D. (2011). A differential evolution based neural network approach to nonlinear system identification. Applied Soft Computing, 11, 861-871.

Vassilopoulos, A.P. and Philippidis, T.P. (2002). Complex stress state effect on fatigue life of GRP laminates. Part I, experimental. International Journal of Fatigue, 24(8), 813-823.

Vassilopoulos, A.P., Georgopoulos, E.F. and Dionysopoulos, V. (2007). Artificial neural networks in spectrum fatigue life prediction of composite materials. International Journal of Fatigue, 29(3), 20-29.

Vassilopoulos, A.P., Georgopoulos, E.F. and Keller, T. (2008). Comparison of genetic programming with conventional methods for fatigue life modeling of FRP composite materials. International Journal of Fatigue, 30(9), 1634-1645.

Vassilopoulos, A.P., Manshadi, B.D. and Keller, T. (2010). Influence of the constant life diagram formulation on the fatigue life prediction of composite materials. International Journal of Fatigue, 32(4), 659-669.

Vassilopoulos, A. P. (2010). Introduction to the fatigue life prediction of composite materials and structures: past, present and future prospects. In A.P. Vassilopoulos (Ed.), Fatigue life prediction of composites and composite structures (pp. 1-38). Cambridge: Woodhead Publishing Limited.

Venkatesh, V. and Rack, H.J. (1999). A neural network approach to elevated temperature creep–fatigue life prediction. International Journal of Fatigue, 21(3), 225-234.

Zhang, Z., and Friedrich, K.. (2003). Artificial neural networks applied to polymer composites: a review. Composites Science and Technology, 63(14), 2029-2044.




DOI: http://dx.doi.org/10.12962/j2746279X.v1i2.16948

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