ANN Modeling for Prediction of Fatigue Crack Growth Rate - Gupta, Saurabh Kumar
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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…mehr

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Produktbeschreibung
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.
Autorenporträt
Dr. Saurabh Kumar Gupta arbeitet als Assistenzprofessor in der Abteilung für Maschinenbau am Raj Kumar Goel Institute of Technology, Ghaziabad. Er hat am Motilal Nehru National Institute of Technology (MNNIT) in Allahabad, Indien, in Maschinenbau promoviert.