Uniaxial Fatigue Analysis, using S-N (stress-life) and E-N (strain-life) approaches for predicting the life (number
of loading cycles) of a structure under cyclical loading may be performed by using HyperLife.
Strain-life analysis is based on the fact that many critical locations such as notch
roots have stress concentration, which will have obvious plastic deformation during
the cyclic loading before fatigue failure. Thus, the
elastic-plastic strain results are essential for performing strain-life analysis.
Multiaxial Fatigue Analysis, using S-N (stress-life), E-N (strain-life), and Dang Van Criterion (Factor
of Safety) approaches for predicting the life (number of loading cycles) of a structure under cyclical
loading may be performed by using HyperLife.
The method implemented in OptiStruct is based on a research paper Fatigue Life Prediction of MAG-Welded Thin-Sheet Structures published by M. Fermér, M Andréasson, and B Frodin.
Uniaxial Fatigue Analysis, using S-N (stress-life) and E-N (strain-life) approaches for predicting the life (number
of loading cycles) of a structure under cyclical loading may be performed by using HyperLife.
The S-N curve, first developed by Wöhler, defines a relationship between stress and number of
cycles to failure. Typically, the S-N curve (and other fatigue properties) of a
material is obtained from experiment; through fully reversed rotating bending tests.
Due to the large amount of scatter that usually accompanies test results,
statistical characterization of the data should also be provided (certainty of
survival is used to modify the S-N curve according to the standard error of the
curve and a higher reliability level requires a larger certainty of survival).
When S-N testing data is presented in a log-log plot of alternating nominal stress amplitude or range versus cycles to failure , the relationship between and can be described by straight line segments.
Normally, a one or two segment idealization is used.
(1)
for segment 1
Where, is the nominal stress range, are the fatigue cycles to failure, is the first fatigue strength exponent, and is the fatigue strength coefficient.
The S-N approach is based on elastic cyclic loading, inferring that the S-N curve should be
confined, on the life axis, to numbers greater than 1000 cycles. This ensures that
no significant plasticity is occurring. This is commonly referred to as
high-cycle fatigue.
S-N curve data is provided for a given material using the Materials module.
Cycle Counting
Cycle counting is used to extract discrete simple "equivalent" constant amplitude cycles from a
random loading sequence. One way to understand "cycle counting" is as a changing
stress-strain versus time signal. Cycle counting will count the number of
stress-strain hysteresis loops and keep track of their range/mean or maximum/minimum
values.
Rainflow cycle counting is the most widely used cycle counting method. It requires that the
stress time history be rearranged so that it contains only the peaks and valleys and
it starts either with the highest peak or the lowest valley (whichever is greater in
absolute magnitude). Then, three consecutive stress points (, , and ) will define two consecutive ranges as and |. A cycle from to is only extracted if . Once a cycle is extracted, the two points forming
the cycle are discarded and the remaining points are connected to each other. This
procedure is repeated until the remaining data points are exhausted.
Equivalent Nominal Stress
Since S-N theory deals with uniaxial stress, the stress components need to be resolved into one combined value for each calculation point, at each time step, and then used as equivalent nominal stress applied on the S-N curve.
Various stress combination types are available with the default being "Absolute maximum principle
stress". "Absolute maximum principle stress" is recommended for brittle materials,
while "Signed von Mises stress" is recommended for ductile material. The sign on the
signed parameters is taken from the sign of the Maximum Absolute Principal
value.
Mean Stress Correction
Generally, S-N curves are obtained from standard experiments with fully reversed
cyclic loading. However, the real fatigue loading could not be fully-reversed, and
the normal mean stresses have significant effect on fatigue performance of
components. Tensile normal mean stresses are detrimental and compressive normal mean
stresses are beneficial, in terms of fatigue strength. Mean stress correction is
used to take into account the effect of non-zero mean stresses.
The Gerber parabola and the Goodman line in Haigh's coordinates are widely used when
considering mean stress influence, and can be expressed as:
Gerber:(2)
Goodman:(3)
Where,
Mean stress given by
Stress Range given by
Stress range after mean stress correction (for a stress range and a mean stress )
Ultimate strength
The Gerber method treats positive and negative mean stress correction in the same way
that mean stress always accelerates fatigue failure, while the Goodman method
ignores the negative means stress. Both methods give conservative result for
compressive means stress. The Goodman method is recommended for brittle material
while the Gerber method is recommended for ductile material. For the Goodman method,
if the tensile means stress is greater than UTS, the damage will be greater than
1.0. For the Gerber method, if the mean stress is greater than UTS, the damage will
be greater than 1.0, with either tensile or compressive.
A Haigh diagram characterizes different combinations of stress amplitude and mean
stress for a given number of cycles to failure.
Parameters affecting mean stress influence can be defined using
the mean stress correction in the Fatigue Module dialog and the Assign Material
dialog.
GERBER2:
Improves the Gerber method by ignoring the effect of negative mean
stress.
SODERBERG:
Is slightly different from GOODMAN; the mean stress is normalized by
yield stress instead of ultimate tensile stress.(4)
Where,
Equivalent stress amplitude
Stress amplitude
Mean stress
Yield stress
FKM:
If only one slope field is specified for mean stress correction, the
corresponding Mean Stress Sensitivity value () for Mean Stress Correction is set equal to Slope in
Regime 2 (Figure 5). Based on FKM-Guidelines, the Haigh diagram is divided into
four regimes based on the Stress ratio () values. The Corrected value is then used to choose
the S-N curve for the damage and life calculation stage.
Note: The FKM equations below illustrate the calculation of Corrected Stress
Amplitude (). The actual value of stress used in the Damage
calculations is the Corrected stress range (which is ). These equations apply for SN curves input by
the user (by default, any user-defined SN curve is expected to be input for a
stress ratio of R=1.0).
There are 2 available options for FKM correction in HyperLife. They are activated by setting FKM MSS to
1 slope/4 slopes in the Assign
Material dialog.
If
only one slope is defined and if mean stress correction on an SN module is set
to FKM:
Regime 1 (R > 1.0)
Regime 2 (-∞ ≤ R ≤ 0.0)
Regime 3 (0.0 < R < 0.5)
Regime 4 (R ≥ 0.5)
Where,
Stress amplitude after mean stress correction (Endurance stress)
Mean stress
Stress amplitude
Slope entered for region 2
If all four slopes are
specified for mean stress correction, the corresponding Mean Stress Sensitivity
values are slopes for controlling all four regimes. Based on FKM-Guidelines, the
Haigh diagram is divided into four regimes based on the Stress ratio () values. The Corrected value is then used to choose
the S-N curve for the damage and life calculation stage.
If
four slopes are defined and mean stress correction is set to
FKM:
Palmgren-Miner's linear damage summation rule is used. Failure is predicted when:(5)
Where,
Materials fatigue life (number of cycles to failure) from its S-N curve
at a combination of stress amplitude and means stress level .
Number of stress cycles at load level .
Cumulative damage under load cycle.
The linear damage summation rule does not take into account the effect of the load sequence on the accumulation of damage, due to cyclic fatigue loading. However, it has been proved to work well for many applications.