A neural network approach to elevated temperature creepâfatigue life prediction

Venkatesh, V.C.

doi:10.1016/s0142-1123(98)00071-1

Cited by 96 publications

(47 citation statements)

References 13 publications

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“…8 by setting total strain range (Δε t ), plastic strain range (Δε p ), tensile hold time (t h ) and compressive hold time (c h ) as input values (O i ) and creep-fatigue life (N f ) as target value (T k ) [11,12]. Data points used for the artificial neural network were shown in Table 3.…”

Section: Life Prediction By the Artificial Neural Network Methodsmentioning

confidence: 99%

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A study of creep-fatigue life prediction using an artificial neural network

Kwon

Lim

2001

Met. Mater. Int.

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In this study, using AISI 316 stainless steel, creep-fatigue tests were carried out under various test conditions (different total strain ranges and hold times) to verify the applicability of the artificial neural network method to creep-fatigue life prediction. Life prediction was also made by the modified Coffin-Manson method and the modified Ostegren method using 21 data points out of a total 27 experimental data points. The six verification data points were carefully chosen for the purpose of evaluating the predictability of each method. The predicted lives were compared with the experimental results and the following conclusions were obtained within the scope of this study. While the creep-fatigue life prediction by the modified Coffin-Manson method and the modified Ostegren method had average errors of 35.8% and 47.7% respectively, the artificial neural network method had only 15.6%. As a result, the artificial neural network method with the adaptive learning rate was found to be far more accurate and effective than any of the others. The validity of the artificial neural network method for life prediction checked with the six verification data points also proved to be very satisfactory.

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Section: Life Prediction By the Artificial Neural Network Methodsmentioning

confidence: 99%

“…The networks have shown remarkable performance when used to model complex linear and nonlinear relationships, especially in the fields of signal processing, non-destructive testing, corrosion life prediction and some other fields of materials science [11,12].…”

Section: Introductionmentioning

confidence: 99%

A study of creep-fatigue life prediction using an artificial neural network

Kwon

Lim

2001

Met. Mater. Int.

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“…The method was also used to investigate and infer the manner in which such material properties are affected by variations in the parameters that are the main governing elements of these properties. Many researchers have indeed pursued such applications in their studies (Bucar et al, 2006;Genel, 2004;Han, 1995;Lee et al, 1999;Liao et al, 2008;Malinov et al, 2001;Mathew et al, 2007;Mathur et al 2007;Park & Kang, 2007;Pleune & Chopra, 2000;Srinivasan et al, 2003;Venkatessh & Rack, 1999). Once the ANN model is trained properly, it will be able to offer an appropriate estimate of the required output using the given input parameters.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks in the Estimation of Mechanical Properties of Materials

Sadati¹,

Kaklar²,

Ghajar³

2011

Artificial Neural Networks - Industrial and Control Engineering Applications

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“…They reported that the ANN model is superior to the other approaches in terms of prediction performance. Venkatesh and Rack [6] presented a back-propagation neural network to predict the fatigue life. The authors found that the proposed neural network can estimate elevated temperature creep-fatigue life of the Ni-based alloy INCONEL 690.…”

Section: Introductionmentioning

confidence: 99%

Feasibility Assessment of Support Vector Regression Models with Immune Algorithms in Predicting Fatigue Life of Composites

Pai

Hong²,

Lai³

et al. 2006

Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)

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Predicting fatigue life of composite materials is essential to increase reliability of manufacturing systems. The predicting techniques for fatigue life of composite materials are not widely investigated. The support vector regression (SVR) is an emerging forecasting technique and has been applied in many areas successfully. Therefore, this study attempts to examine the feasibility of SVR in predicting the fatigue life of composite materials. Additionally, immune algorithms (IA) are used to select three parameters of SVR models. An experimental data set from a laboratory was employed to depict the feasibility of develpoed SVRIA (support vector regression with immune algorithms) approach in predicting fatigue life of composite materials. Empirical results indicate that the SVRIA is a valid way in predicting fatigue life of composite materials.

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A neural network approach to elevated temperature creepâfatigue life prediction

Cited by 96 publications

References 13 publications

A study of creep-fatigue life prediction using an artificial neural network

A study of creep-fatigue life prediction using an artificial neural network

Application of Artificial Neural Networks in the Estimation of Mechanical Properties of Materials

Feasibility Assessment of Support Vector Regression Models with Immune Algorithms in Predicting Fatigue Life of Composites

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A neural network approach to elevated temperature creepâfatigue life prediction

Cited by 96 publications

References 13 publications

A study of creep-fatigue life prediction using an artificial neural network

A study of creep-fatigue life prediction using an artificial neural network

Application of Artificial Neural Networks in the Estimation of Mechanical Properties of Materials

Feasibility Assessment of Support Vector Regression Models with Immune Algorithms in Predicting Fatigue Life of Composites

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A neural network approach to elevated temperature creepâfatigue life prediction