The elucidation of load history editing effect on fatigue crack growth by crack closure concept

Ko, Soon-Gyu; Oh, Chung-Seog; Choi, Byung-Il

doi:10.1016/j.ijfatigue.2004.07.009

Cited by 13 publications

(7 citation statements)

References 19 publications

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“…Watson and Topper suggested another mean stress correction model, known as the SWT parameter [23], which is defined mathematically as in Eq. (2).…”

Section: Fatigue Damagementioning

confidence: 99%

“…There are requirements that should be considered when generating a new fatigue random load history, such as [3]: (1) sequence of load cycles should be maintained, (2) damage resulting from the new load history should be the same as the original load history, and (3) the new load history should be shorter in length than the original load history to decrease the testing time, by deleting small load cycles and merging smaller ones. Considering the requirements, the simulated strain signals were able to maintain the original cycle sequences, the actual and simulated strain signals had equivalent fatigue damage, and the simulated strain signals decreased the fatigue testing time.…”

Section: Fatigue Lifementioning

confidence: 99%

“…With respect to this problem, it is seen that the fatigue damage due to low amplitude cycles is very small. Hence, the automotive engineers are striving to completely remove low amplitude cycles from initial histories and accelerate fatigue tests for saving time and money [2,3]. This technique is called fatigue data editing (FDE).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fatigue Feature Clustering of Modified Automotive Strain Signals for Saving Testing Time

Husaini¹,

Putra²,

Ali³

2018

Int. J. Automot. Mech. Eng.

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This paper discusses on the clustering of damaging fragments from a fatigue data editing leading to fatigue damage using the fuzzy C-means. In this work, strain signals were measured from an automotive coil spring which involved vehicle movements on three types of road surfaces at different speeds. Next, the extractions of higher amplitude cycle utilising the wavelet transform were conducted. From the results, it was obtained that the transformation was able to shorten the strain signal time up to 81.5 % removing 81.5 % of lower amplitude cycles, which these cycles theoretically contribute to minimum fatigue damage. Therefore, retaining of fatigue damage by more than 90.4 % was obtained. Furthermore, the resulted fragments from the extraction processes had been clustered utilising the fuzzy C-means, providing a significant coefficient of determination, reaching 0.9036. The testing time was successfully decreased up to 81.4 % using the edited strain signals. In conclusion, the technique could be successfully used to shorten a strain signal without changing the main history.

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“…Watson and Topper suggested another mean stress correction model, known as the SWT parameter [23], which is defined mathematically as in Eq. (2).…”

Section: Fatigue Damagementioning

confidence: 99%

Section: Fatigue Lifementioning

confidence: 99%

See 1 more Smart Citation

Fatigue Feature Clustering of Modified Automotive Strain Signals for Saving Testing Time

Husaini¹,

Putra²,

Ali³

2018

Int. J. Automot. Mech. Eng.

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show abstract

“…The time-domain model is actually the most used approach for spectrum editing process as reported in revious studies. [6][7][8][9][10][11][12][13] However, the time-domain method is not precise in predicting the fatigue damage. This is due to the lack of frequency information in the time domain as lowfrequency segments are necessary to calculate the fatigue damage.…”

Section: Introductionmentioning

confidence: 99%

“…The damage reduction is then compared with original fatigue test results. The time‐domain model is actually the most used approach for spectrum editing process as reported in revious studies 6–13 …”

Section: Introductionmentioning

confidence: 99%

Systematic fatigue spectrum editing by fast wavelet transform and genetic algorithm

Mohseni

Santhanam

Williams

et al. 2021

Fatigue Fract Eng Mat Struct

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Fatigue testing is critical in order to establish the service life of load-bearing components and structures. The extensive time associated with full fatigue spectrum testing can lead to prohibitive costs. A significant need exists for a fatigue load spectrum editing methodology, based on the mechanics of fatigue, that produces load spectra that can replicate service damage in laboratory testing and can lead to compressed testing times and reduced costs. In this work, a wavelet genetic (WAVEGEN) algorithm is developed to edit fatigue loading spectra using wavelet analysis to greatly reduce the length of a spectrum while retaining the same damage accumulation characteristics. In addition, an optimization protocol using a genetic algorithm is included within this process to automatically select the best wavelet editing parameters. The algorithm is designed to identify the most suitable wavelet type, filter, and level to optimally edit a given fatigue spectrum and ensure equivalence between edited and unedited spectra from a damage perspective. The algorithm was applied to two well-known aircraft fatigue spectra: Fighter Aircraft Loading Standard for Fatigue evaluation (FALSTAFF) and Transport Wing Standard (TWIST). The proposed approach has demonstrated that both spectra can be compressed significantly even while ensuring equivalence from a damage perspective.

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A review of the wavelet transform for durability and structural health monitoring in automotive applications

Chin,

Abdullah,

Ariffin

et al. 2024

Alexandria Engineering Journal

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The elucidation of load history editing effect on fatigue crack growth by crack closure concept

Cited by 13 publications

References 19 publications

Fatigue Feature Clustering of Modified Automotive Strain Signals for Saving Testing Time

Fatigue Feature Clustering of Modified Automotive Strain Signals for Saving Testing Time

Systematic fatigue spectrum editing by fast wavelet transform and genetic algorithm

A review of the wavelet transform for durability and structural health monitoring in automotive applications

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