Closure to “Discussion of ‘Cumulative Fatigue Damage With Random Loading’” (1962, ASME J. Basic Eng., 84, p. 409)

A method is presented that leads to accurate estimation of the cumulative fatigue damage incurred in a randomly loaded structural element when loading is given in the form of spectral density load, or stress, plots. The load plots are here approximated by a series of straight lines and a closed formula is obtained to yield the damage incurred by the load within each straight line segment. The method avoids the errors that result from human misjudgment in the commonly used curve-stepping approach. It is also adaptable for computer applications and can be incorporated in a stress calculation program to save on computation time. In comparison to curve stepping, five straight-line segments may give the same accuracy as a hundred curve steps. This contrast, however, depends on the degree of irregularity of the load curve.

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Cumulative Fatigue Damage With Random Loading

Journal of Basic Engineering ◽

10.1115/1.3657337 ◽

1962 ◽

Vol 84 (3) ◽

pp. 403-408 ◽

Cited By ~ 6

Author(s):

R. R. Gatts

Keyword(s):

Fatigue Damage ◽

Stress Amplitude ◽

Amplitude Distribution ◽

General Concept ◽

Time Function ◽

Discrete Distributions ◽

Random Loading ◽

Linear Rule ◽

Cumulative Fatigue Damage ◽

Distribution Of Stress

A general concept of the accumulation of fatigue damage is applied where stress amplitude is a random time function with a specified amplitude distribution. A differential equation relating damage accumulation to the amplitude distribution of stress is derived. This equation is applicable to both continuous and discrete distributions. Solutions of the equation are used to predict life under random loading on the basis of constant amplitude S-N data. Such predictions are compared for both continuous and discrete stress amplitude distributions and found in better over-all agreement with the data than comparable predictions by the linear rule.

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Cumulative Fatigue Damage Under Stress-Controlled Conditions

Journal of Basic Engineering ◽

10.1115/1.3425328 ◽

1971 ◽

Vol 93 (4) ◽

pp. 691-698 ◽

Cited By ~ 37

Author(s):

Thang Bui Quoc ◽

J. Dubuc ◽

A. Bazergui ◽

A. Biron

Keyword(s):

Theoretical Analysis ◽

Fatigue Damage ◽

Experimental Results ◽

Published Data ◽

Mean Stress ◽

Remaining Life ◽

Random Loading ◽

Controlled Conditions ◽

Cumulative Fatigue Damage ◽

Good Agreement

A theoretical analysis of uniaxial cumulative fatigue damage is presented together with a large number of experimental results on unnotched specimens of A-201 and A-517 steels. The theory developed permits the prediction of fatigue curves for stress-controlled conditions with zero or positive mean stress as well as the evaluation of the damage accumulated during a fatigue test and hence the prediction of the remaining life of a specimen. Theory is in good agreement with the experimental results as well as with published data on other materials. The development may be extended to other types of tests such as strain-controlled or random loading conditions.

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Nonlinear Fatigue Damage Accumulation Under Random Loading

Journal of Pressure Vessel Technology ◽

10.1115/1.2842176 ◽

1996 ◽

Vol 118 (2) ◽

pp. 168-173 ◽

Cited By ~ 2

Author(s):

W. Q. Zhu ◽

M. X. Jiang

Keyword(s):

Fatigue Damage ◽

Damage Accumulation ◽

Stochastic Theory ◽

Reliability Function ◽

Random Loading ◽

Fatigue Damage Accumulation ◽

Probability Densities ◽

Cumulative Fatigue Damage ◽

Analytical Expressions ◽

Damage Rule

The analytical expressions for the probability densities of the cumulative fatigue damage and fatigue life and for the reliability function are obtained for a mechanical or structural component subject to stationary random stress process on the basis of a stochastic theory of fatigue damage accumulation proposed by the first author and his co-worker and the Morrow’s nonlinear damage rule. The comparison between the results from Morrow’s and Palmgren-Miner’s damage rules for the case when the stress is a narrow-band stationary Gaussian process with zero mean is made and some important conclusions are drawn.

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