Cumulative Damage and Effect of Mean Strain in Low-Cycle Fatigue of a 2024-T351 Aluminum Alloy

Ohji, Kiyotsugu; Miller, William R.; Marin, Joseph

doi:10.1115/1.3645963

Cited by 36 publications

(8 citation statements)

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“…The sequence effects of Fig. 2 do not occur if all of the levels cause significant plastic straining (13)(14)(15)(16)(17), which is consistent with the observation that the initiation period is short in the low cycle region.…”

Section: Sequence Effectssupporting

confidence: 73%

Fatigue Failure Predictions for Complicated Stress-Strain Histories

Dowling¹

1971

239

146

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Section: Sequence Effectssupporting

confidence: 73%

Fatigue Failure Predictions for Complicated Stress-Strain Histories

Dowling¹

1971

239

146

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“…12 Some investigators have studied the effects of the strain ratio R for aluminium metals. 27,28 The experimental results showed that the compressive mean strain on lowcycle fatigue has the same effect as the tensile mean strain only if their magnitudes were the same. The following empirical relationship has been suggested by Ohji et al 28 between the total strain range ε and the number of cycles to failure N f , which accounts for the variation in the strain ratio R as follows:…”

Section: E F F E C T O F S T R a I N R A T I O ( R)mentioning

confidence: 99%

“…The experimental results showed that the compressive mean strain on low‐cycle fatigue has the same effect as the tensile mean strain only if their magnitudes were the same. The following empirical relationship has been suggested by Ohji et al 28 between the total strain range Δɛ and the number of cycles to failure N f , which accounts for the variation in the strain ratio R as follows: where, a = (−1/ c ); material constant equal to the negative reciprocal of the fatigue ductility exponent c . …”

Section: Effect Of Strain Ratio (R)mentioning

confidence: 99%

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Low‐cycle fatigue life behaviour of BS 460B and BS B500B steel reinforcing bars

Hawileh

Abdalla

Oudah

et al. 2010

Fatigue Fract Eng Mat Struct

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A B S T R A C T This paper investigates the low-cycle fatigue resistance of BS 460B and BS B500B steel reinforcing bars and proposes models for predicting their fatigue life based on plasticstrain (ε ap ) and total-strain (ε a ) amplitudes. Constant-amplitude, strain-controlled lowcycle fatigue tests were carried out on these bars under cyclic load with a frequency of 0.05 Hz. The maximum applied axial strain amplitude (ε s,max ) ranges from 3 to 10% with zero and non-zero mean strains. The strain ratios (R = ε s,min /ε s,max ) used are R = −1, −0.5 and 0. Hysteresis loops were recorded and plastic and total strain amplitudes were related to the number of reversals (2N f ) to fatigue failure and models for predicting the number of reversals to fatigue failure were proposed. It is concluded that the predicted fatigue life of these bars is very accurate when compared with the measured experimental fatigue life results for wide range of values of strain ratios. It is also observed that based on plastic-strain amplitude, BS B500B consistently has a longer life (higher number of cycles to failure) than those of BS 460B for all R values; however, at low plastic-strain amplitudes they tend to behave similarly, irrespective of R value. Other observations and conclusions were also drawn. E s = steel modulus of elasticity N f = number of cycles to failure σ max = maximum tensile stress σ mean = mean stress and is equal to (σ max + σ min )/2 σ f = fatigue strength coefficient ε ap = applied plastic strain amplitude ε p,max = maximum applied plastic strain ε p,min = minimum applied plastic strain ε a = total (elastic + plastic) applied strain amplitude ε s,max = maximum applied total (elastic + plastic) strain ε s,min = minimum applied total (elastic + plastic) strain ε f = empirical constant known as the fatigue ductility coefficient I N T R O D U C T I O NLow-cycle fatigue is a major fracture criterion of reinforcing steel bars when subjected to cyclic (seismic) loading. The cyclic stress-strain behaviour of reinforcing steel bars is an important parameter in moment-curvature analysis of reinforced concrete structures under seismic load.Steel reinforcing bars in concrete buildings are likely to be subjected to low-cycle fatigue loading due to earthquakes or rotating heavy machineries. Such cyclic loading may reduce fatigue strength or cause fracture of the reinforcing bars. The low-cycle fatigue characteristics of steel reinforcing bars that are commonly used in Europe and the Middle East such as BS4449 460B 1 and B500B 2 are not well studied for a wide range of strain ratios. Therefore, this study is needed to investigate the

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“…The need to track stress-strain around a hysteresis curve to determine mean stress is taken out of the picture, making the embedded process easier to implement. One such empirical relationship between total strain range, Δε, and cycles to failure is adopted herein [15]. This empirical relationship which accounts for mean strain instead of mean stress is written as ′ (2) where =…”

Section: Damage Accumulationmentioning

confidence: 99%

Fatigue Life Monitoring of Metallic Structures by Decentralized Rainflow Counting Embedded in a Wireless Sensor Network

O’Connor

Kim

Lynch

et al. 2010

ASME 2010 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, Volume 2

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Fatigue is one of the most widespread damage mechanisms found in metallic structures. Fatigue is an accumulated degradation process that occurs under cyclic loading, eventually inducing cracking at stress concentration points. Fatigue-related cracking in operating structures is closely related with statistical loading characteristics, such as the number of load cycles, cycle amplitudes and means. With fatigue cracking a prevalent failure mechanism of many engineered structures including ships, bridges and machines, among others, a reliable method of fatigue life estimation is direly needed for future structural health monitoring systems. In this study, a strategy for fatigue life estimation by a wireless sensor network installed in a structure for autonomous health monitoring is proposed. Specifically, the computational resources available at the sensor node are leveraged to compress raw strain time histories of a structure into a more meaningful and compressed form. Simultaneous strain sensing and on-board rainflow counting are conducted at individual wireless sensors with fatigue life prediction made using extracted amplitudes and means. These parameters are continuously updated during long-term monitoring of the structure. Histograms of strain amplitudes and means stored in the wireless sensor represent a highly compressed form of the original raw data. Communication of the histogram only needs to be done by request, dramatically reducing power consumption in the wireless sensing network. Experimental tests with aluminum specimens in the laboratory are executed for verification of the proposed damage detection strategy.

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Cumulative Damage and Effect of Mean Strain in Low-Cycle Fatigue of a 2024-T351 Aluminum Alloy

Cited by 36 publications

References 0 publications

Fatigue Failure Predictions for Complicated Stress-Strain Histories

Fatigue Failure Predictions for Complicated Stress-Strain Histories

Low‐cycle fatigue life behaviour of BS 460B and BS B500B steel reinforcing bars

Fatigue Life Monitoring of Metallic Structures by Decentralized Rainflow Counting Embedded in a Wireless Sensor Network

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