Fatigue of four metallic materials under asynchronous loadings: Small cracks observation and fatigue life prediction

Pejkowski, Łukasz; Seyda, Jan

doi:10.1016/j.ijfatigue.2020.105904

Cited by 15 publications

(5 citation statements)

References 56 publications

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“…This explains the results of the previous analyzes of fatigue life prediction methods. They showed that the application of the fatigue damage parameter, based on the maximum shear strain, resulted in the most accurate estimation of the fatigue life [14].…”

Section: Discussionmentioning

confidence: 99%

“…This work is a continuation of the research aimed to study the multiaxial fatigue of metallic alloys in the elastic-plastic strain range. In the previous papers, the influence of additional non-proportional hardening on the fatigue life was studied [12,13], the directions of initiation and growth of small cracks on the specimens' surfaces were observed [14], and, initially, the development of fatigue cracks during the fatigue tests using the replication technique was studied [15].…”

Section: Methodsmentioning

confidence: 99%

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SEM analysis of PA38-T6 aluminum alloy thin-walled tubular specimen fatigue fracture, and comparison to surface replication results

Seyda

Pejkowski

2021

MATEC Web Conf.

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The paper presents results of SEM analysis of fatigue fracture surface of thin-walled tubular specimen manufactured from PA38-T6 aluminum alloy. In the previous research specimens were subjected to multiaxial loadings. They were axial, torsional, in-phase, 90º out-of-phase and asynchronous loadings. During the fatigue experiments, the process of initiation and development of cracks was tracked using the surface replication technique, using cellulose acetate thin foils. In the present work, SEM observations of fatigue fracture surface were conducted and compared to previously obtained data. It was confirmed, that the dominating mechanism of fatigue crack formation was the coalescence of small crack, regardless of loading type and level.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

SEM analysis of PA38-T6 aluminum alloy thin-walled tubular specimen fatigue fracture, and comparison to surface replication results

Seyda

Pejkowski

2021

MATEC Web Conf.

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“…The material parameters and fatigue test results of the PA38‐T6 aluminum alloy (equivalent to 6060 grade) used in the paper came from the authors' earlier work 50 . The details of fatigue life prediction procedures and results are also available in Pejkoski et al 51 This material was selected for the purposes of the present work since it was well recognized in previous studies. The fully reversed sine‐shaped strain loadings were applied in the studies:

ε ((), t) goodbreak= ε_{a} \sin ((), 2 π f_{ε} t)

γ ((), t) goodbreak= γ_{a} \sin ((), 2 π f_{γ} t goodbreak+ δ)

where

ε_{a}

γ_{a}

f_{ε}

, and

f_{γ}

are amplitudes and frequencies of normal and shear strain, respectively, and

δ

is a phase shift angle.…”

Section: Experimental Datamentioning

confidence: 99%

Application of machine learning methods in multiaxial fatigue life prediction

Pałczyński

Skibicki

Pejkowski

et al. 2022

Fatigue Fract Eng Mat Struct

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This paper compares the results of multiaxial fatigue life estimation using machine learning methods and classical fatigue models. The fatigue life of PA38-T6 aluminum alloy under uniaxial, proportional, and non-proportional loading, including asynchronous loading, is studied. Machine learning methods are trained only on basic loadings, namely, axial, torsional, and 90 out-of-phase. The results obtained with the machine learning algorithms, dense neural networks, support vector regression (with linear and radial basis functions), decision tree, random forest, and XGBoost algorithms, are comparable to the results of classical models like Ellyin-Goło s, Fatemi-Socie, and Ince-Glinka. The best results are achieved for dense neural networks. In that case, they are often slightly more accurate than those obtained using classical methods.

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“…The fatigue damage parameter is used to reduce the spatial stress or strain state to a scalar quantity of the dimensions of stress, strain, or energy. It is then compared with the reference regression curves to calculate the fatigue life [ 33 , 34 ]. Uniaxial regression curves show an explicit relationship between the applied stress, strain, or energy values and the number of cycles to failure.…”

Section: Brief Review Of Fatigue Life Prediction Modelsmentioning

confidence: 99%

Gaussian Process for Machine Learning-Based Fatigue Life Prediction Model under Multiaxial Stress–Strain Conditions

2022

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In this paper, a new method for fatigue life prediction under multiaxial stress-strain conditions is developed. The method applies machine learning with the Gaussian process for regression to build a fatigue model. The fatigue failure mechanisms are reflected in the model by the application of the physics-based stress and strain invariants as input quantities. The application of the machine learning algorithm solved the problem of assigning an adequate parametric fatigue model to given material and loading conditions. The model was verified using the experimental data on the CuZn37 brass subjected to various cyclic loadings, including non-proportional multiaxial strain paths. The performance of the machine learning-based fatigue life prediction model is higher than the performance of the well-known parametric models.

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Fatigue of four metallic materials under asynchronous loadings: Small cracks observation and fatigue life prediction

Cited by 15 publications

References 56 publications

SEM analysis of PA38-T6 aluminum alloy thin-walled tubular specimen fatigue fracture, and comparison to surface replication results

SEM analysis of PA38-T6 aluminum alloy thin-walled tubular specimen fatigue fracture, and comparison to surface replication results

Application of machine learning methods in multiaxial fatigue life prediction

Gaussian Process for Machine Learning-Based Fatigue Life Prediction Model under Multiaxial Stress–Strain Conditions

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