Comparison of several methods for the notch effect quantification on specimens from 2124-T851 aluminum alloy

Papuga, Jan; Karkulín, Adam; Hanžl, Ondřej; Lutovinov, Maxim

doi:10.1016/j.prostr.2019.12.044

Cited by 8 publications

(3 citation statements)

References 2 publications

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“…The created dataset is available to readers in the MS Excel file attached to this document. To complete it, the datasets extracted from papers and reports 27,[30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48] were compiled. Focusing on uniaxial fatigue strength values (rather than multiaxial load cases) allowed the authors to supplement the dataset with additional elements from the following areas.…”

Section: The Used Datasetmentioning

confidence: 99%

Application of machine learning models for estimating the material parameters for multiaxial fatigue strength calculation

Nagode,

Papuga,

Oman

2023

Fatigue Fract Eng Mat Struct

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This paper deals with a practical task of estimating missing material fatigue strengths required for the evaluation of multiaxial fatigue strength criteria, knowing other static or fatigue material parameters. Instead of searching for various analytical equations describing the dependencies between different material parameters, several machine learning models implemented in the caret R package are used here. The dataset used to train and test these models is based on the FatLim dataset with different material parameters, which has been redesigned for this new purpose. It is demonstrated that substantially more data points, such as were available in this study, are needed to achieve the goal set here. Although the results obtained at the current scale may be improved by the addition of new data points, the best performance of the random forest model rf and the worst performance of the pcr model are evident.

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Section: The Used Datasetmentioning

confidence: 99%

Application of machine learning models for estimating the material parameters for multiaxial fatigue strength calculation

Nagode,

Papuga,

Oman

2023

Fatigue Fract Eng Mat Struct

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“…Finding the correct value of the notch effect using computer simulation is a challenge that is hard to overcome [6]. However, the model creation procedure offers a number of parameters that have a significant impact on the result obtained.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Stress Concentrations in Notches

Chmelko

Žlábek

Margetin

et al. 2021

Metals

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The fatigue life curves of materials are very sensitive to the magnitude of the stress amplitude. A small change or inaccuracy in the determination of the stress value causes large changes or inaccuracies in the calculated fatigue life estimate. Therefore, the use of computer simulations for fatigue life estimation requires a proper model development methodology. The paper is devoted to the problem of the modeling of components in notches using FEM. The modeling parameters significantly influencing the obtained stress results have been defined. Exact analytical solutions served as a benchmark for comparing the accuracy of the stress values obtained using FEM models. For the selected 2D and 3D notched components, diagrams were created for sensitivity analysis of the influence of the mesh element density at the root of the notch in correlation with the exact analytical solution. The findings from model building were applied to model the stress concentration at the root of a V-weld joint in a gas pipeline.

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“…Typically, there is not a separate experimentally defined S‐N curve available for each stress amplitude and mean value combination, and an approximation routine must be used to obtain the equivalent stress amplitude (see, e.g., Dowling 14 or Papuga et al 15 ). Because the basic case treated here avoids the effect of the stress gradient 16 (only smooth unnotched specimens are referred to), the only remaining unknown is the question of the right damage accumulation model. Because of its simplicity, the Palmgren–Miner rule is taken as the optimum solution, though it is known to be imperfect 17 .…”

Section: Introductionmentioning

confidence: 99%

Various parameters of the multiaxial variable amplitude loading and their effect on fatigue life and fatigue life computation

Papuga

Margetin

Chmelko

2021

Fatigue Fract Eng Mat Struct

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The paper discusses various partial solutions used for estimating fatigue life under variable amplitude multiaxial loading in the high‐cycle fatigue domain. The concurring effects are treated, and their proposed solutions are commented upon. The major focus is on the categories of the phase shift effect and of cycle counting and on the scope and quality of data, which support discussed theories. Results of own new experimental data set on specimens from S355 steel are provided. Fatigue life estimates for McDiarmid and Findley multiaxial methods and for two different methods of load path decomposition to cycles are shown to highlight some of the points open for discussion. It is concluded that the available experimental data are not sufficient to substantiate a clear decision to follow a definite algorithm.

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Comparison of several methods for the notch effect quantification on specimens from 2124-T851 aluminum alloy

Cited by 8 publications

References 2 publications

Application of machine learning models for estimating the material parameters for multiaxial fatigue strength calculation

Application of machine learning models for estimating the material parameters for multiaxial fatigue strength calculation

Simulation of Stress Concentrations in Notches

Various parameters of the multiaxial variable amplitude loading and their effect on fatigue life and fatigue life computation

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