A Model Based Study of Fatigue Life Prediction for Multifarious Loadings

Kant, Chandra; Harmain, G.A.

doi:10.4028/www.scientific.net/kem.882.296

Cited by 5 publications

(2 citation statements)

References 158 publications

(185 reference statements)

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“…Note that in accordance with [9,17], the strain-based approach follows the same modelling pattern, based on compatibility, as that applied to an extended stress-based approach, which includes the LCF, HCF and VHCF domains, as well as the influence of the stress ratio, R, see [18], but even including the influence of the test frequency [12,19]. Consideration of the different fatigue zones is also analysed in [10,20].…”

Section: Different Methods For Working With Aimentioning

confidence: 99%

Automatic machine learning versus human knowledge-based models, property-based models and the fatigue problem

Castillo,

Fernández Canteli,

Muñiz Calvente

2023

Phil. Trans. R. Soc. A.

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This paper is devoted to emphasizing the importance of human-based knowledge and to present the original property-based models. The main idea is that models are built in terms of equations that reproduce and guarantee their satisfaction, which leads to non-arbitrary parametric models. The methods based on data alone are not sufficient to be applied to fatigue S–N and GRV-N models: first, the required high number of results in machine learning is not attained in fatigue; second, a black box cannot supply a comprehension of the fatigue phenomenon; third, the absence of a supporting model impedes extrapolation beyond the scope of experimentation; and fourth, many other data are required to include the stress ratio, R , while robust models are already available. These models are artificial intelligence mixed models, where its main part is human-based whereas the parameter estimation is solved based on data. The fatigue problem is used to illustrate the methodology and show that its generalization is applicable to other real problems. A detailed analysis of the fatigue model properties is done to show the readers how to extend those properties to other cases. Some future lines of research are suggested, followed by some conclusions. This article is part of the theme issue ‘Physics-informed machine learning and its structural integrity applications (Part 2)’.

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Section: Different Methods For Working With Aimentioning

confidence: 99%

Automatic machine learning versus human knowledge-based models, property-based models and the fatigue problem

Castillo,

Fernández Canteli,

Muñiz Calvente

2023

Phil. Trans. R. Soc. A.

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“…Some researchers such as Cruces and others studied the influence of deformation history on the fatigue behavior of 316 and 304 austenitic stainless steels [12]. Kant C and al [13] investigated the life prediction models considering the variability of fluctuating loads. In their study, the modes were based on the concept of crack propagation, load interaction, number of cycles, stress and variable amplitude loading with crack closure.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Fatigue Life and Crack Growth in Austenitic Stainless Steel AISI 304l

2023

jjmie

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This paper focuses on life prediction models considering the variability of loads. It describes the fatigue strength and crack growth behavior of AISI 304L austenitic stainless steel at room temperature. The deformation behavior of the austenitic stainless steel was investigated via tensile and fatigue tests. Crack tests were performed on CT50 specimens for constant ΔP and ΔK under different cyclic loads before the specimen was severely cracked by a monotonically increasing load. The impact of loading sequence and the evolution of the dissipated energy ΔJ were investigated using constant charge ΔP and stress intensity ΔK. The dissipated energy per cycle is used to predict fatigue crack growth. Experimental results proved that the various parameters considered in terms of cyclic loading and the results obtained under monotonic loading up to unstable failure show that cracking at constant ∆P is significantly more damaging than at constant ∆K. Therefore, cyclic loading with a constant pressure drop will result in faster crack growth or shorter fatigue life than loading with a constant stress intensity factor ΔK.

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Fatigue Life Prediction Under Interspersed Overload in Constant Amplitude Loading Spectrum via Crack Closure and Plastic Zone Interaction Models—A Comparative Study

Kant

Harmain

2022

Lecture Notes in Mechanical Engineering

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A Model Based Study of Fatigue Life Prediction for Multifarious Loadings

Cited by 5 publications

References 158 publications

Automatic machine learning versus human knowledge-based models, property-based models and the fatigue problem

Automatic machine learning versus human knowledge-based models, property-based models and the fatigue problem

Analysis of Fatigue Life and Crack Growth in Austenitic Stainless Steel AISI 304l

Fatigue Life Prediction Under Interspersed Overload in Constant Amplitude Loading Spectrum via Crack Closure and Plastic Zone Interaction Models—A Comparative Study

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