Surface roughness influence in micromechanical fatigue lifetime prediction with crystal plasticity models for steel

Natkowski, Erik; Sonnweber-Ribic, Petra; Münstermann, Sebastian

doi:10.1016/j.ijfatigue.2022.106792

Cited by 11 publications

(10 citation statements)

References 77 publications

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“…Since SRVE is much smaller than the macro model, the influence of cylinder surface curvature can be ignored. 20 To verify the sensitivity of surface morphology to the boundary conditions of the submodel, two SRVE models were established with the same conditions except for the different positions of the peaks and valleys. The stress cloud map of the simulation results is shown in Figure 7B.…”

Section: Microstructure Instantiationsmentioning

confidence: 99%

“…C 11 , C 12 , and C 44 can be obtained by simultaneous determination of three independent Equations ( 19), (20), and (21). The system of equations produces two sets of solutions, one of which can be eliminated as it fails the mechanical stability requirements for cubic materials.…”

Section: Crystal Plasticity Modeling and Parameter Calibrationmentioning

confidence: 99%

“…In recent years, some scholars have also conducted crystal plasticity modeling based on the surface properties of material processing: William et al 19 introduced a method to incorporate shot‐peened residual stresses within a three‐dimensional (3D) polycrystalline microstructure and simulated the scattering of residual stress after a single loading/unloading cycle. Natkowski et al 20 described a framework to include surface roughness in crystal plasticity simulations with emphasis on enabling extensive computational studies. Zhang et al 21 established a two‐dimensional crystal plasticity model based on the surface integrity of AISI4310 steel after milling, and the fatigue life of the material was predicted by simulating its uniaxial tension–compression fatigue behavior.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A crystal plastic finite element model for the effect of surface integrity on multiaxial fatigue life after multistage machining processes

Liang,

Jiao,

Yan

et al. 2024

Fatigue Fract Eng Mat Struct

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Surface integrity influences the material fatigue performance significantly, but most micromechanical models based on crystal plasticity only consider the microstructure of the matrix. In this paper, a crystal plastic finite element modeling method was proposed, which simultaneously considered surface roughness, residual stress, and gradual microstructure. It was verified through multiaxial fatigue experiments of materials processed by different multistage machining processes. The results show that larger axial residual compressive stress and smaller grain size can lead to higher multiaxial fatigue life. The existence of residual compressive stress causes the location of material crack initiation to decrease from the surface layer to the subsurface layer. The proposed model can predict the multiaxial fatigue life of materials within a 50% error band.

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Section: Microstructure Instantiationsmentioning

confidence: 99%

Section: Crystal Plasticity Modeling and Parameter Calibrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A crystal plastic finite element model for the effect of surface integrity on multiaxial fatigue life after multistage machining processes

Liang,

Jiao,

Yan

et al. 2024

Fatigue Fract Eng Mat Struct

View full text Add to dashboard Cite

show abstract

“…Current physical-motivated methods require tremendous modeling and computation effort to predict the fatigue strength for a broad range of metallic materials due to complex multiscale impact of the component's geometry, applied load, surface quality, microstructure characteristics, and so forth. [1][2][3][4][5] Different guidelines have been developed for assessing the component fatigue strength. [6][7][8][9][10][11] However, these guidelines are often empirically derived and practically focused.…”

Section: Introductionmentioning

confidence: 99%

“…Developing structural components requires consideration of various influencing factors for reliable design. Current physical‐motivated methods require tremendous modeling and computation effort to predict the fatigue strength for a broad range of metallic materials due to complex multiscale impact of the component's geometry, applied load, surface quality, microstructure characteristics, and so forth 1–5 . Different guidelines have been developed for assessing the component fatigue strength 6–11 .…”

Section: Introductionmentioning

confidence: 99%

Analysis of data‐driven models for predicting fatigue strength of steel components with uncertainty quantification

Frie,

Kolyshkin,

Eberl

2023

Fatigue Fract Eng Mat Struct

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Material informatics has emerged as a valuable research field in material science, providing solutions to previously unsolvable problems or accelerating deliverables. Fatigue failure, as a complex and non‐deterministic phenomenon, requires a probabilistic approach to assess the uncertainty of the fatigue strength prediction. This study compares various probabilistic data‐driven models for credible fatigue strength predictions for three distinct steel groups. The analysis considers data and model uncertainty, evaluating their impacts on predictive quality from engineering and data science perspectives. Results reveal that deep ensembles outperform other probabilistic models regarding negative log‐likelihood (NLL), while random forest exhibits the lowest root mean square error (RMSE). Notably, the prediction accuracy of case‐hardened steels is negatively affected by insufficient material properties definitions, while stainless steels demonstrate the best performance compared to other steel types.

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Effect of Rolling on Microstructure Evolution and Plastic Deformation Behavior of A473M Martensitic Stainless Steel

Wang

Zhang

Wang

et al. 2022

steel research int.

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Herein, the surface of A473M martensitic stainless steel is strengthened by rolling processing technology. The microstructure evolution and plastic deformation behavior are characterized by electron backscatter diffraction (EBSD). The experimental results indicate that residual compressive stress will be formed on the surface of A473M steel after rolling, and the maximum value can reach 946 MPa. The surface roughness decreases significantly from 383 to 62.7 nm, which is only 1/5 of the matrix. The microstructure of the rolled samples is obviously refined, and <101>//ND texture is formed. The nanohardness of the hardened layer on the sample surface increases from 2.5 to 4.8 GPa, and the elastic modulus increases from 140 to 217 GPa. Compared with the matrix, the tensile strength and yield strength after rolling are increased by 40% and 22%, respectively. EBSD analysis indicates that the texture of the rolled samples after tensile changed from the <101>//TD direction of the matrix to the <101>//TD and <111>//RD directions.

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Surface roughness influence in micromechanical fatigue lifetime prediction with crystal plasticity models for steel

Cited by 11 publications

References 77 publications

A crystal plastic finite element model for the effect of surface integrity on multiaxial fatigue life after multistage machining processes

A crystal plastic finite element model for the effect of surface integrity on multiaxial fatigue life after multistage machining processes

Analysis of data‐driven models for predicting fatigue strength of steel components with uncertainty quantification

Effect of Rolling on Microstructure Evolution and Plastic Deformation Behavior of A473M Martensitic Stainless Steel

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