Artificial Neural Network Assessment System for Fatigue Life

The purpose of this work was to predict the fatigue life of pre-corroded LC4 aluminum alloy by applying artificial neural network (ANN). Specimens were exposed to the same corrosive environment for 24h, 48h, and 72h. Fatigue tests were conducted under different stress levels. The existing experimental data sets were used for training and testing the construction of proposed network. A suitable network architecture (2-15-1) was proposed with good performance in this study. For evaluating the method efficiency, the experimental results have been compared to values predicted by ANN. The maximum absolute relative error for predicted values does not exceed 5%. Therefore it can be concluded that using neural networks to predict the fatigue life of LC4 is feasible and reliable.

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The prediction of fatigue life of muffler by artificial neural network

Journal of the Korean Society of Marine Engineering ◽

10.5916/jkosme.2013.37.8.869 ◽

2013 ◽

Vol 37 (8) ◽

pp. 869-876

Author(s):

Soon-Cheol Park ◽

Sung-Su Kang ◽

Jin-Ho Yoon ◽

Gug-Yong Kim

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fatigue Life ◽

Artificial Neural

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Prediction of composite fatigue life under variable amplitude loading using artificial neural network trained by genetic algorithm

10.1063/1.5030241 ◽

2018 ◽

Author(s):

Muhamad Nur Rohman ◽

Mas Irfan P. Hidayat ◽

Agung Purniawan

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Fatigue Life ◽

Variable Amplitude Loading ◽

Variable Amplitude ◽

Artificial Neural

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Application of Artificial Neural Network to Forecast the Tensile Fatigue Life of Carbon Material

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.385-387.533 ◽

2008 ◽

Vol 385-387 ◽

pp. 533-536 ◽

Cited By ~ 2

Author(s):

Xiao Ling Liao ◽

Wen Feng Xu ◽

Zhi Qiang Gao

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fatigue Life ◽

Electrical Resistance ◽

Carbon Materials ◽

Materials Science ◽

Failure Detection ◽

Fatigue Loading ◽

Tensile Fatigue ◽

Artificial Neural

Artificial neural network (ANN) is widely applied to the modeling of complex systems, which has become a common modeling method in the study of materials science. As the ideal candidates for high temperature structural materials, carbon materials are no doubt involved in fatigue loads, so the study on forecasting fatigue life is meaningful. In this paper, the electrical resistance at various fatigue cycles and level of applied stress of the materials under tensile fatigue loading has been detected, and regarded the fracture or fatigue cycles equal to 106 as fatigue life of carbon materials. On the basis of the electrical resistance value, the fatigue life has been forecasted by applied the ANN. The results indicated that the ANN could forecast the fatigue life of carbon materials well; finally, the applications of ANN in the study of material, such as properties prediction, damage prediction and failure detection were reviewed.

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