Surface stress concentration factor via Fourier representation and its application for machined surfaces

Cheng, Zhengkun; Liao, Ridong; Lu, Wei

doi:10.1016/j.ijsolstr.2017.01.023

Cited by 27 publications

(20 citation statements)

References 24 publications

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Section: Fatigue Life Estimations Based On Surface Roughnessmentioning

confidence: 99%

“…Surface morphologies can be considered as multi-notches, which can introduce stress concentrations and affect the fatigue behaviors of specimens. The SCFs induced by surface topography are as follows [12]: The mean bending principal strain at the dangerous point around the fillet of the stepped shaft is zero during the bending fatigue test. By setting the maximum bending principal strain, ε θmax , shown in Figure 5e as the strain amplitude and substituting it into Equation (2), the fatigue lives of the smooth stepped shafts under cyclic bending load can be predicted; the predicted fatigue life of the smooth stepped shaft is 1,662,300 reversals.…”

Section: Fatigue Notch Factor Of Rough Specimensmentioning

confidence: 99%

“…The finite element descriptions of surface topography to calculate the SCF were then put forward [7][8][9][10][11]. Recently, the theoretical formulas to obtain the SCF imposed by surface morphology based on Fourier representations were built as well [12][13][14][15]. Nevertheless, the SCF introduced by surface roughness is not enough to estimate the fatigue strength of rough specimens due to notch effects [16].…”

Section: Introductionmentioning

confidence: 99%

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Fatigue Life Prediction of Machined Specimens with the Consideration of Surface Roughness

Zhu

Zhang

et al. 2021

Materials

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The fatigue strength and fatigue life of high-strength steels are greatly affected by their surface roughness. This study investigates the underlying mechanisms responsible for fatigue failure of the high-strength steel 42CrMo. Bending fatigue tests of stepped shafts with different levels of surface roughness were conducted to observe the fatigue live reduction affected by surface topography. Besides, the mechanical properties of 42CrMo and its strain–life relationship were established. Moreover, the analytical formulas to describe the stress concentration factor (SCF) and fatigue notch factor (FNF) induced by surface topography were introduced. To estimate the fatigue life of machined specimens with the consideration of surface roughness, the elastic portion of the total strain–life curve of the material was revised with the proposed analytical FNF imposed by surface topography. Comparisons between the estimated fatigue lives and experimentally obtained fatigue lives show that the effect of surface roughness on fatigue lives could be estimated effectively and conveniently by the proposed procedure.

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…”

Section: Fatigue Life Estimations Based On Surface Roughnessmentioning

confidence: 99%

Section: Fatigue Notch Factor Of Rough Specimensmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fatigue Life Prediction of Machined Specimens with the Consideration of Surface Roughness

Zhu

Zhang

et al. 2021

Materials

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“…From a technical perspective, having a theoretical approach to consider the resolution definition of the surface roughness in fatigue limit predictions is very appealing. Recently, the stress concentration for 2D and 3D machined surfaces have been comprehensively investigated, and analytical solutions via Fourier representation were derived as well 31–35 . However, the analytical derivation about the stress concentration for predicting the fatigue limit of machined round bars is still lacking.…”

Section: Introductionmentioning

confidence: 99%

Analytical prediction of the fatigue limit for axisymmetric round bars with rough surface morphology

Cheng

Zhu

Zhang

et al. 2021

Fatigue Fract Eng Mat Struct

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Analytical approaches to calculating stress distribution in machined round bars with slightly axisymmetric surface topography were derived. Surface morphologies of machined round bars were simplified as axisymmetric geometries and simulated in the form of Fourier representation. According to the point method of the theory of critical distance (TCD), an analytical expression for predicting the fatigue notch factor (FNF) of rough round specimens was proposed as well. The effectiveness of the proposed analytical expression was well verified by finite element method (FEM), with relative errors of less than 15%. Besides, the definition of the upper cut‐off frequency for estimating the fatigue limit of rough round bars was discussed comprehensively. Results showed that stress concentration factor (SCF) was sensitive to the upper cut‐off frequency and unable to be convergent, while FNF tended to be convergent and was mainly determined by low frequencies of surface topography. It explained why the height parameters of surface morphologies are the most significant characteristic parameters in determining fatigue behaviors.

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“…Therefore, cast mechanical components are often additionally surface finished by machining or by polishing, in order to counteract the detrimental surface roughness effect. Thus, many studies contribute to the surface roughness effect on fatigue strength, investigating machined surfaces obtaining periodic surface textures [24][25][26][27][28][29]. As in the literature [30,31] various analytical equations are introduced to characterise geometrical notches, these formulations are adapted to assess machined [32][33][34][35] or even cast [36][37][38], and more recently also additively manufactured [39] surfaces.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Surface Layer Fatigue Strength Assessment of EN AC-46200 Sand Castings

Pomberger

Oberreiter

Leitner

et al. 2020

Metals

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The local fatigue strength within the aluminium cast surface layer is affected strongly by surface layer porosity and cast surface texture based notches. This article perpetuates the scientific methodology of a previously published fatigue assessment model of sand cast aluminium surface layers in T6 heat treatment condition. A new sampling position with significantly different surface roughness is investigated and the model exponents a 1 and a 2 are re-parametrised to be suited for a significantly increased range of surface roughness values. Furthermore, the fatigue assessment model of specimens in hot isostatic pressing (HIP) heat treatment condition is studied for all sampling positions. The obtained long life fatigue strength results are approximately 6% to 9% conservative, thus proven valid within an range of 30 µm ≤ S v ≤ 260 µm notch valley depth. To enhance engineering feasibility even further, the local concept is extended by a probabilistic approach invoking extreme value statistics. A bivariate distribution enables an advanced probabilistic long life fatigue strength of cast surface textures, based on statistically derived parameters such as extremal valley depth S v i and equivalent notch root radius ρ ¯ i . Summing up, a statistically driven fatigue strength assessment tool of sand cast aluminium surfaces has been developed and features an engineering friendly design method.

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Surface stress concentration factor via Fourier representation and its application for machined surfaces

Cited by 27 publications

References 24 publications

Fatigue Life Prediction of Machined Specimens with the Consideration of Surface Roughness

Fatigue Life Prediction of Machined Specimens with the Consideration of Surface Roughness

Analytical prediction of the fatigue limit for axisymmetric round bars with rough surface morphology

Probabilistic Surface Layer Fatigue Strength Assessment of EN AC-46200 Sand Castings

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