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Purpose: In this work, fatigue crack propagation life of 7020 T7 and 2024 T3 aluminum alloys under the influence of load ratio was predicted by using artificial neural network (ANN). Design/methodology/approach: Numerous phenomenological models have been proposed for predicting fatigue life of the components under the influence of load ratio to take into account the mean load effect. Findings: In current research, an automatic prediction methodology has been adopted to estimate the constant amplitude loading fatigue life under the above condition by applying artificial neural network (ANN). Practical implications: ANNs show great potential for predicting fatigue crack growth rate especially by interpolation within the tested range. However, its benefit is lost when the model is needed to extrapolate the available experimental data. Originality/value: The predicted results are found to be in good agreement with the experimental findings when tested on two aluminum alloys 7020 T7 and 2024 T3 respectively.
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Rocznik
Tom
Strony
133--138
Opis fizyczny
Bibliogr. 23 poz., tab., rys., wykr.
Twórcy
autor
autor
autor
autor
- Department of Metallurgical and Materials Engineering, National Institute of Technology, Rourkela 769008, India, guddy95@gmail.com
Bibliografia
- [1] H. Al-Nashash, Y. Al-Assaf, B. Lvov, W. Mansoor, Laser speckle for materials classification utilizing wavelets and neural networks image processing techniques, Journal Materials Evaluation 59 (2001) 1072-1078.
- [2] P. Artymiak, L. Bukowski, J. Feliks, S. Narberhaus, H. Zenner, Determination of S-N curves with the application of artificial neural networks, Fatigue and Fracture of Engineering Materials and Structures 22 (1999) 723-728.
- [3] F. Aymerich, M. Serra, Prediction of fatigue strength of composite laminates by means of neural networks, Key Engineering Materials 144 (1998) 231-240.
- [4] W.F. Brown, J.E. Srawley, Plane strain crack toughness testing of high strength metallic materials, ASTM STP, American Society for Testing and Materials, Philadelphia, 1966, 1.
- [5] Y. Cheng, W.L. Huang, C.Y. Zhou, Artificial neural network technology for the data processing of on-line corrosion fatigue crack growth monitoring, International Journal of Pressure Vessels and Piping 76 (1999) 113-116.
- [6] S. Dinda, D. Kujawski, Correlation and prediction of fatigue crack growth for different R-ratios using Kmax and ΔK+ parameters, Engineering Fracture Mechanics 71 (2004) 1779-1790.
- [7] K. Donald, P.C. Paris, An evaluation of ΔKeff estimation procedures on 6060-T6 and 2024-T3 aluminum alloys, International Journal of Fatigue 21 (1999) 47-57.
- [8] W. Elber, The significance of fatigue crack closure. In: Damage tolerance in aircraft structures, ASTM STP 486, American Society for Testing and Materials, Philadelphia, 1971, 230-242.
- [9] M.E. Haque, K.V. Sudhakar, Prediction of corrosion-fatigue behavior of DP steel through artificial neural network, International Journal of Fatigue 23 (2001) 1-4.
- [10] S. Haykin, Neural Network, A Comprehensive Foundation, Prentice Hall, 1999.
- [11] R. Herzallah, Y. Al-Assaf, Control of non-linear and time-variant dynamic systems using neural networks, Proceedings of the 4th World Multiconference “Systemics, Cybernetics and Informatics”, Florida, 2000.
- [12] J.Y. Kang, J.H. Song, Neural network applications in determining the fatigue crack opening load, International Journal of Fatigue 20/1 (1998) 57-69.
- [13] M. Klesnil, P. Lukas, Effect of stress cycle asymmetry on fatigue crack growth, Materials Science Engineering 9 (1972) 231-240.
- [14] D. Kujawski, A new (ΔK+Kmax) 0.5 driving force parameter for crack growth in aluminum alloys, International Journal of Fatigue 23 (2001) 733-740.
- [15] D. Kujawski, F. Ellyin, A fatigue crack growth model with load ratio effects, Engineering Fracture Mechanics 28 (1987) 367-378.
- [16] J.A. Lee, D.P. Almond, B. Harris, The use of neural networks for the prediction of fatigue lives of composite materials, Applied Science Manufacturing 30 (1999) 1159-1169.
- [17] C.S. Lee, W. Hwang, H.C. Park, K.S. Han, Failure of carbon/epoxy composite tubes under combined axial and torsional loading-1. Experimental results and prediction of biaxial strength by the use of neural networks, Composites Science and Technology 59 (1999) 1779-1788.
- [18] W. Mansoor, H. Al-Nashash, Y. Al-Assaf, Image classification using wavelets and neural networks, Proceedings of the 18th IASTED International Conference “Applied Informatics”, Austria, 2000.
- [19] R.M.V. Pidaparti, M.J. Palakal, Neural Network Approach to Fatigue-Crack-Growth Predictions under Aircraft Spectrum Loadings, Journal of Aircraft 32/4 (1995) 825-831.
- [20] T.T. Pleune, O.K. Chora, Using artificial neural networks to predict the fatigue life of carbon and low-alloy steels, Nuclear Engineering and Design 197 (2000) 1-12.
- [21] K. Sadananda, A.K. Vasudevan, R.L. Holtz, E.U. Lee, Analysis of overload effects and related phenomenan, International Journal of Fatigue 21 (1999) 233-246.
- [22] V. Venkatesh, H.J. Rack, A neural network approach to elevated temperature creep-fatigue life prediction, International Journal of Fatigue 21 (1999) 225-234.
- [23] ASTM E647-00, Standard test method for measurement of fatigue crack growth rates, American Society for Testing & Materials, West Conshohocken.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA9-0042-0017