Artificial neural network for random fatigue loading analysis including the effect of mean stress

2019 ◽

Vol 233 (23-24) ◽

pp. 7525-7544

Author(s):

JF Durodola

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Frequency Domain ◽

Fatigue Loading ◽

Neural Network Approach ◽

Frequency Domain Methods ◽

Artificial Neural ◽

Non Gaussian ◽

Random Fatigue ◽

Loading Analysis

There has been a lot of work done on the analysis of Gaussian loading analysis perhaps because its occurrence is more common than non-Gaussian loading problems. It is nevertheless known that non-Gaussian load occurs in many instances especially in various forms of transport, land, sea and space. Part of the challenge with non-Gaussian loading analysis is the increased number of variables that are needed to model the loading adequately. Artificial neural network approach provides a versatile means to develop models that may require many input variables in order to achieve applicable predictive generalisation capabilities. Artificial neural network has been shown to perform much better than existing frequency domain methods for random fatigue loading under stationary Gaussian load forms especially when mean stress effects are included. This paper presents an artificial neural network model with greater predictive capability than existing frequency domain methods for both Gaussian and non-Gaussian loading analysis. Both platykurtic and leptokurtic non-Gaussian loading cases were considered to demonstrate the scope of application. The model was also validated with available SAE experimental data, even though the skewness and kurtosis of the signal in this case were mild.

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A pattern recognition artificial neural network method for random fatigue loading life prediction

International Journal of Fatigue ◽

10.1016/j.ijfatigue.2017.02.003 ◽

2017 ◽

Vol 99 ◽

pp. 55-67 ◽

Cited By ~ 20

Author(s):

J.F. Durodola ◽

N. Li ◽

S. Ramachandra ◽

A.N. Thite

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Artificial Neural Network ◽

Life Prediction ◽

Fatigue Loading ◽

Neural Network Method ◽

Network Method ◽

Artificial Neural Network Method ◽

Artificial Neural ◽

Random Fatigue

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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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Application of ANN Back-Propagation for Fracture Design Parameters of Medium Carbon Steel in Extra-Low Cycle Axial Fatigue Loading

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.345-346.445 ◽

2007 ◽

Vol 345-346 ◽

pp. 445-448

Author(s):

Hong Yan Duan ◽

You Tang Li ◽

Shuai Tan

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Network Model ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

Fatigue Loading ◽

Medium Carbon Steel ◽

Training Data ◽

Design Parameters ◽

Artificial Neural

The fracture problems of medium carbon steel under extra-low cycle axial fatigue loading were studied using artificial neural network in this paper. The training data were used in the formation of training set of artificial neural network. The artificial neural network model exhibited excellent comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. Training artificial neural network model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, notch depth and tip radius of the notch, and the output, the cycle times of fracture were used during the network training. The neural network architecture is designed. The artificial neural network model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. The result show that the training model has good performance, and the experimental data and predicted data from artificial neural network are in good coherence.

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