Evaluation of Strain–Life Fatigue Curve Estimation Methods and Their Application to a Direct-Quenched High-Strength Steel

  • M. Dabiri
  • M. Ghafouri
  • H. R. Rohani Raftar
  • T. Björk
Article
  • 18 Downloads

Abstract

Methods to estimate the strain–life curve, which were divided into three categories: simple approximations, artificial neural network-based approaches and continuum damage mechanics models, were examined, and their accuracy was assessed in strain–life evaluation of a direct-quenched high-strength steel. All the prediction methods claim to be able to perform low-cycle fatigue analysis using available or easily obtainable material properties, thus eliminating the need for costly and time-consuming fatigue tests. Simple approximations were able to estimate the strain–life curve with satisfactory accuracy using only monotonic properties. The tested neural network-based model, although yielding acceptable results for the material in question, was found to be overly sensitive to the data sets used for training and showed an inconsistency in estimation of the fatigue life and fatigue properties. The studied continuum damage-based model was able to produce a curve detecting early stages of crack initiation. This model requires more experimental data for calibration than approaches using simple approximations. As a result of the different theories underlying the analyzed methods, the different approaches have different strengths and weaknesses. However, it was found that the group of parametric equations categorized as simple approximations are the easiest for practical use, with their applicability having already been verified for a broad range of materials.

Keywords

artificial neural network continuum damage mechanics high-strength steel low-cycle fatigue life prediction 

Abbreviations

AISI

American Iron and Steel Institute

ANN

Artificial neural network

ASTM

American Society for Testing and Materials

BPA

Backpropagation algorithm

CDM

Continuum damage mechanics

CEV

Carbon equivalent value

HB

Brinell hardness

HV

Vickers hardness

LCF

Low-cycle fatigue

RA

Reduction in area

SAE

Society of Automotive Engineers

SAV

Strain amplitude variation

Symbols

b

Fatigue strength exponent

c

Fatigue ductility exponent

C

Constant parameter

C1, C2

Material constants

D

Damage variable

Dc

Critical damage

DN

Damage corresponding to N cycles

D0

Initial damage

E

Young’s modulus

E′

Effective Young’s modulus

K

Strength coefficient

K′

Cyclic strength coefficient

n

Strain hardening exponent

n′

Cyclic strain hardening exponent

N

Number of cycles

Nt

Transition fatigue life

2Nf

Reversals to failure

R

Correlation coefficient

Se

Endurance limit

\(\Delta \varepsilon\)

Strain range

\(\varepsilon_{\text{a}}\)

Strain amplitude

\(\varepsilon_{\text{e}}\)

Elastic strain

\(\varepsilon_{ est }\)

Estimated strain

\(\varepsilon_{\text{exp}}\)

Experimental strain

\(\varepsilon_{\text{f}}\)

True fracture strain

\(\varepsilon_{\text{p}}\)

Plastic strain

\(\varepsilon_{\text{f}}^{{\prime }}\)

Fatigue ductility coefficient

\(\varepsilon_{0}\)

Threshold strain

\(\sigma\)

Nominal stress

\(\tilde{\sigma }\)

Effective stress

\(\sigma_{\text{ave}}\)

Average strength

\(\sigma_{\text{f}}\)

True fracture strength

\(\sigma_{\text{u}}\)

Ultimate tensile strength

\(\sigma_{\text{y}}\)

Yield strength

\(\sigma_{\text{f}}^{{\prime }}\)

Fatigue strength coefficient

\(\sigma_{\text{y}}^{{\prime }}\)

Cyclic yield strength

Notes

Acknowledgments

This study was performed as part of the Breakthrough Steels and Applications (BSA) program funded by the Finnish Funding Agency for Innovation (TEKES) and the Digital, Internet, Materials and Engineering Co-Creation (DIMECC) platform.

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Copyright information

© ASM International 2018

Authors and Affiliations

  1. 1.Laboratory of Steel StructuresLappeenranta University of TechnologyLappeenrantaFinland
  2. 2.Department of Mechanical Engineering, Science and Research BranchIslamic Azad UniversityTehranIran

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