Experimental and FEM numerical assessment of multiaxial fatigue failure criteria for a rolling Stock's seats structure

Sepe, Raffaele; Greco, Alessandro; Luca, A. De; Armentani, Enrico; Berto, Filippo

doi:10.1016/j.engfailanal.2019.04.065

Cited by 19 publications

(7 citation statements)

References 18 publications

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“…Engineering components generally fail due to irreversible micro-plastic deformation from which cracks are nucleated, generally from notches or at critical locations [6][7][8]. Considering the inhomogeneous microstructure and non-uniform mechanical properties of metal materials, crack tips and defects may encounter diversiform microstructures, in turn demonstrating variable strength against Fatigue Crack-Growth (FCG), as if "the cracks would pass through a different material possessing different properties" [9].…”

Section: Introductionmentioning

confidence: 99%

On the statistical nature of fatigue crack-growth through Monte Carlo simulations and experimental data

Giannella

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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Understanding factors that contribute to scatter in crack-growth rates data is fundamental to reduce risk of unexpected structural failures. This document discusses the statistical nature of material scattering observed in fatigue crack propagation tests through a comparison between Monte Carlo simulations and experimental test data. The case study consists of constant amplitude tests on central cracked 2024-T3 aluminium alloy plate specimens loaded in pure Mode-I with stress ratio R equals to 0.2. It was found that results of simulations matched the experimental fatigue lives in terms of both mean value and dispersion around it. Based on these outcomes, the crack-growth behaviour can be described in a statistical manner with the expectation to reduce the inaccuracy present in life predictions.

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Section: Introductionmentioning

confidence: 99%

On the statistical nature of fatigue crack-growth through Monte Carlo simulations and experimental data

Giannella

2022

IOP Conf. Ser.: Mater. Sci. Eng.

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“…A crack can be a negative repercussion of an external reason like a fatigue load. [8][9][10][11][12] Crack onset and propagation must be properly identified, and Structural Health Monitoring systems can be implemented on the to be monitored part. [13][14][15][16][17][18][19] However, the extraction of the main crack-related features is a tricky task.…”

Section: Introductionmentioning

confidence: 99%

Application of Supervised and Unsupervised Machine Learning to the Classification of Damaged Rotor‐Bearing Systems

Rezazadeh,

Felaco,

Fallahy

et al. 2023

Macromolecular Symposia

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In this letter, supervised machine learning classifiers are compared with clustering algorithms in the categorization of malfunctioned rotor systems. For this purpose, a dataset containing 660 faulted rotor‐bearing systems—that are, unbalanced, misaligned, and cracked—is used. Samples are created utilizing the finite element method in MATLAB. A sequential forward selection (SFS) method is employed to reduce the number of features in the signal‐processing stage after the feature extraction phase in the time, frequency, and time–frequency domains. The outcomes of three supervised algorithms—support vector machine, k‐nearest neighbors, and ensemble learning, as well as one unsupervised procedure, e.g., k‐Means clustering, are compared. In the latter method, to find the optimal number of clusters, the Calinski–Harabasz criterion is applied. The findings represent that, even though the supervised methods' acquired accuracies are noticeably higher (97.7% in the validation stage), using clustering algorithms can be beneficial in a variety of real‐time condition monitoring applications in rotating machines where the type, extent, and location of the damage are unknown.

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“…Engineering components generally fail due to irreversible micro-plastic deformations from which cracks are nucleated, generally at notches or at critical locations (Sepe et al 2019;Romano et al 2020;Giannella et al 2018a). This process is followed by fatigue crack-growth (FCG) through the cross-section and, eventually, by the final failure of the whole component.…”

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confidence: 99%

“…Several studies have explored the possibility to choose safety factors from a probabilistic design perspective (Sepe et al 2019;Grell and Laz 2010;Guida and Penta 2010;Beretta and Regazzi 2016). Most of the available research focused primarily on obtaining life predictions by considering slight perturbations on the loading conditions as well as small variations on the Crack-Growth Rates (CGRs) (Grell and Laz 2010;Guida and Penta 2010).…”

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confidence: 99%

“…Even though advanced probabilistic approaches can provide the engineers with more robust information about the safety of structures (Zhu et al 2017(Zhu et al , 2018aArmentani et al 2020;Niu et al 2021;Klawonn et al 2020;Beretta et al 2016a;Giannella 2021a;Sepe et al 2019;Romano et al 2020;Giannella et al 2018a;Grell and Laz 2010;Guida and Penta 2010;Beretta and Regazzi 2016;Beretta and Carboni 2006), as a downside, these approaches often require vast sets of experimental data for their development to limit the statistical uncertainty. The material under analysis selected for this work was the 25CrMo4 steel (known also as EA4T grade in the railway industry), for which a vast set of FCG test data at R ¼ À 1 was available in literature (Luke et al 2011;Na ´hlı ´k et al 2017;Pokorny ´et al 2016a;Beretta et al 2016b;Maierhofer et al 2014;Wu et al 2016;Varfolomeev et al 2010;Project MARAXIL;Hu et al 2021), see Fig.…”

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confidence: 99%

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Uncertainty quantification in fatigue crack-growth predictions

Giannella

2022

Int J Fract

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The reliability of the damage tolerance approach to engineering design is affected by numerous sources of uncertainty that can lead to unsafe predictions, in turn jeopardizing the safety of structures. This work presents a robust stochastic framework for fatigue crack-growth predictions applied to a round bar under tension–compression loading conditions. Multi-source uncertainties were taken into account to derive the lifespan distribution for the bar in such a way to cover the uncertainties typically appearing in a structural integrity assessment. The sensitivity of each input variable was obtained and the influences of variables on the life predictions were derived and ranked accordingly.

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Experimental and FEM numerical assessment of multiaxial fatigue failure criteria for a rolling Stock's seats structure

Cited by 19 publications

References 18 publications

On the statistical nature of fatigue crack-growth through Monte Carlo simulations and experimental data

On the statistical nature of fatigue crack-growth through Monte Carlo simulations and experimental data

Application of Supervised and Unsupervised Machine Learning to the Classification of Damaged Rotor‐Bearing Systems

Uncertainty quantification in fatigue crack-growth predictions

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