Estimation methods for fatigue properties of steels under axial and torsional loading

Kim, K.S.; Chen, X.; Han, Chul–Hi; Lee, H.W.

doi:10.1016/s0142-1123(01)00190-6

Cited by 108 publications

(76 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It requires, in the first place, the four empirical constants that must be obtained from fatigue tests. Furthermore, estimating cyclic stress-strain curves based on fatigue properties could lead to considerable errors in certain situations (Kim et al, 2002). So, it is recommended that the values of K' and n' obtained from direct fitting of the experimental data are used in fatigue design rather than those calculated from Eq.…”

Section: Case Study: Estimation Of Cyclic Strain Hardening Exponent Amentioning

confidence: 99%

“…The K' and n' are estimated by two separate networks. For these estimations steels tensile data used as input to the ANN model, are extracted from the literature (SAE, 2001;Kim et al, 2002;Roessle & Fatemi, 2000). In order to enhance training performance, both input and output variables are normalized before the network is trained.…”

Section: Case Study: Estimation Of Cyclic Strain Hardening Exponent Amentioning

confidence: 99%

“…For K' estimation, the properties of 48 steels reported in the literature (SAE, 2001;Kim et al, 2002;Roessle & Fatemi, 2000) were used. 36 values of data were used for training the network and the others consisting of 12 data values were used for testing the trained network.…”

Section: Wwwintechopencommentioning

confidence: 99%

See 2 more Smart Citations

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

View full text Add to dashboard Cite

Section: Case Study: Estimation Of Cyclic Strain Hardening Exponent Amentioning

confidence: 99%

Section: Case Study: Estimation Of Cyclic Strain Hardening Exponent Amentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

“…The Roessle-Fatemi method was shown to result in close predictions for uniaxial fatigue tests on 69 commonly used steels. This method has been widely used by many researchers and many studies such as [7][8][9][10] have shown good prediction results using this method.…”

Section: Introductionmentioning

confidence: 99%

“…The most notable study on the estimation of shear fatigue properties of steels was conducted by Kim et al [7]. In their study uniaxial and torsional monotonic and fatigue tests were conducted on eight ductile steels and several methods for estimating uniaxial fatigue properties were evaluated.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Estimation Methods for Shear Fatigue Properties and Correlations with Uniaxial Fatigue Properties for Steels and Titanium Alloys

Sharifimehr

Fatemi

2018

MATEC Web Conf.

View full text Add to dashboard Cite

Abstract. The goal of this study was to evaluate the accuracy of different methods in correlating uniaxial fatigue properties to shear fatigue properties, as well as finding a reliable estimation method which is able to predict the shear fatigue behavior of steels and titanium alloys from their monotonic properties. In order to do so, axial monotonic as well as axial and torsion fatigue tests were performed on two types of steel and a Ti-6Al-4V alloy. The results of these tests along with test results of 23 types of carbon steel, Inconel 718, and three types of titanium alloys commonly used in the industry were analyzed. It was found that von Mises and maximum principal strain criteria were able to effectively correlate uniaxial fatigue properties to shear fatigue properties for ductile and brittle behaving materials, respectively. Also, it was observed that for steels and Inconel 718 obtaining shear fatigue properties from uniaxial fatigue properties which are in turn calculated from Roessle-Fatemi estimation method resulted in reasonable estimations when compared to experimentally obtained uniaxial fatigue properties. Furthermore, a modification was made to the Roessle-Fatemi hardness method in order to adjust it to fatigue behavior of titanium alloys. The modified method, which was derived from uniaxial fatigue properties of titanium alloys with Brinell hardness between 240 and 353 proved to be accurate in predicting the shear fatigue behaviors.

show abstract

Estimation of the cyclic shear stress‐strain curve from tensile properties for steels

Li¹,

Zhang

2018

Materialwissenschaft Werkst

View full text Add to dashboard Cite

The objective of this study is to derive a method for estimating the cyclic shear deformation properties (the cyclic shear strength coefficient and cyclic shear strain hardening exponent) of steels from common tensile properties. For this purpose, equations that relate the cyclic shear strength coefficient and cyclic shear strain hardening exponent to the monotonic tensile properties (the ultimate tensile strength and monotonic yield strength) are established on the basis of the 15 kinds of steel taken from the technical literature. Validity of some of the more commonly used methods of estimating cyclic shear deformation properties is examined and compared to the correlations proposed in this study. The proposed method is shown to be better used to estimate the cyclic shear deformation properties.

show abstract

Estimation methods for fatigue properties of steels under axial and torsional loading

Cited by 108 publications

References 11 publications

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

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

Evaluation of Estimation Methods for Shear Fatigue Properties and Correlations with Uniaxial Fatigue Properties for Steels and Titanium Alloys

Estimation of the cyclic shear stress‐strain curve from tensile properties for steels

Contact Info

Product

Resources

About