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ISBN 10: 6139930693 ISBN 13: 9786139930692
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Añadir al carritoTaschenbuch. Condición: Neu. ANN Modeling for Prediction of Fatigue Crack Growth Rate | Saurabh Kumar Gupta | Taschenbuch | 64 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786139930692 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.
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Publicado por LAP LAMBERT Academic Publishing Okt 2018, 2018
ISBN 10: 6139930693 ISBN 13: 9786139930692
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Fatigue crack growth is one of the most important factors in the design of the different mechanical structures. Different models were developed to predict the fatigue crack growth rate. These models cannot be used for different materials to predict the fatigue crack growth rate and examine the effect of different parameters.The neural network is a complicated nonlinear dynamic system with the ability of prediction based on real time information. It is a good tool to develop quantitative predictive method for the fatigue crack growth rate based on experimental data. The prediction of crack retardation using ANN shows greater accuracy as compared to the wheeler model. The overload application reduces the crack growth and results in enhanced fatigue life. 64 pp. Englisch.
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ISBN 10: 6139930693 ISBN 13: 9786139930692
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Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Gupta Saurabh KumarDr. Saurabh Kumar Gupta is working as an Assistant Professor in the Department of Mechanical Engineering Raj Kumar Goel Institute of Technology, Ghaziabad. He has done Ph.D. & M.Tech in Mechanical Engineering from .
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ISBN 10: 6139930693 ISBN 13: 9786139930692
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Fatigue crack growth is one of the most important factors in the design of the different mechanical structures. Different models were developed to predict the fatigue crack growth rate. These models cannot be used for different materials to predict the fatigue crack growth rate and examine the effect of different parameters.The neural network is a complicated nonlinear dynamic system with the ability of prediction based on real time information. It is a good tool to develop quantitative predictive method for the fatigue crack growth rate based on experimental data. The prediction of crack retardation using ANN shows greater accuracy as compared to the wheeler model. The overload application reduces the crack growth and results in enhanced fatigue life.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 64 pp. Englisch.
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ISBN 10: 6139930693 ISBN 13: 9786139930692
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Fatigue crack growth is one of the most important factors in the design of the different mechanical structures. Different models were developed to predict the fatigue crack growth rate. These models cannot be used for different materials to predict the fatigue crack growth rate and examine the effect of different parameters.The neural network is a complicated nonlinear dynamic system with the ability of prediction based on real time information. It is a good tool to develop quantitative predictive method for the fatigue crack growth rate based on experimental data. The prediction of crack retardation using ANN shows greater accuracy as compared to the wheeler model. The overload application reduces the crack growth and results in enhanced fatigue life.
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ISBN 10: 6139930693 ISBN 13: 9786139930692
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