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Tham khảo tài liệu 'artificial neural networks industrial and control engineering applications part 5', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Application of Artificial Neural Networks in the Estimation of Mechanical Properties of Materials 129 problems in particular. A general overview of the neural network models is given followed by the introduction of a case study related to some fatigue properties of steels. It is emphasized that neural network models are effective techniques for modelling the problems in material science as the technique will help a material scientist with the determination and estimation of the complex and often nonlinear relationship governing the input output data obtained within an experimental setup. As such neural network techniques are still an ongoing research area as applied to the problems in material science and engineering. 6. References Abdalla J. A. Hawileh Rami. in press . Modeling and simulation of low-cycle fatigue life of steel reinforcing bars using artifiial neural network. Journal of the Franklin Institute ISSN 0016-0032 Bahrami A. Mousavi Anijdan S. H. Ekrami A. 2005 . Prediction of Mechanical Properties of DP Steels Using Neural Network Model. Journal of Alloys and Compounds Vol.392 No.1-2 April 2005 pp. 177-182 ISSN 0925-8388 Bucar T. Nagode M. Fajdiga M. 2006 . A Neural Network Approach to Describing the Scatter of S-N Curves. International Journal of Fatigue Vol.28 No.4 April 2006 pp. 311-323 ISSN 0142-1123 Fogel D. B. 1994 . An Introduction to Simulated Evolutionary Optimization. IEEE Transactions on Neural Networks Vol.5 No.1 1994 pp. 3-14 ISSN 1045-9227 Genel K. 2004 . Application of Artificial Neural Network for Predicting Strain-Life Fatigue Properties of Steels on the Basis of Tensile Data. International Journal of Fatigue Vol.26 No.10 October 2004 pp. 1027-1035 ISSN 0142-1123 Ghajar R. Alizadeh J. Naserifar N. 2008 . Estimation of cyclic strain hardening exponent and cyclic strength coefficient of steels by artificial neural networks Proceedings of ASME 2008 International Mechanical Engineering Congress and Exposition pp. 639-648 ISBN .
