A comparative study for selecting and using simulation methods of Gaussian random surfaces

Wang, Yuechang; Azam, Abdullah; Wilson, Mark C. T.; Neville, Anne; Morina, Ardian

doi:10.1016/j.triboint.2021.107347

Cited by 14 publications

(8 citation statements)

References 39 publications

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“… The question of whether the bijection is constituted between the reconstruction feature parameters and the contact feature parameters. Note that He [72] and Wang [73] have given a corresponding analysis based on the reconstruction model of FFT method, but there is a curious problem. If the error between the autocorrelation function of the reconstructed surface and the specified autocorrelation function is within a controlled range, then according to the theory, their spatial features should have been similar and the corresponding contact feature parameters should be the consistent, and there should be no difference between different reconstruction method.…”

Section: Discussionmentioning

confidence: 99%

Literature review on engineering surface modeling

Maa¹,

Lib²

2023

MethodsX

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Section: Discussionmentioning

confidence: 99%

Literature review on engineering surface modeling

Maa¹,

Lib²

2023

MethodsX

View full text Add to dashboard Cite

“…30 Surface roughness is the most commonly used statistic to characterize surface morphology, and the autocorrelation length ratio can greatly distinguish between turning and grinding surfaces. [31][32][33][34] So these two statistics measured from the processed surface are used to generate feature surfaces, which can largely characterize the original surface.…”

Section: Grain Orientationmentioning

confidence: 99%

“…For a more detailed process, please refer to Wang's research. 31 Finally, the generated feature surface is integrated into the SRVE by calling a script to adjust the mesh node coordinates. The comparison between the integrated SRVE surface morphology feature values and experimental results is shown in Figure 6A,B.…”

Section: Microstructure Instantiationsmentioning

confidence: 99%

“…The autocorrelation length of the grinding surface in two directions has little difference, which is related to the fact that the surface is composed of lots of numerous minor scratches between grits and the workpiece 30 . Surface roughness is the most commonly used statistic to characterize surface morphology, and the autocorrelation length ratio can greatly distinguish between turning and grinding surfaces 31–34 . So these two statistics measured from the processed surface are used to generate feature surfaces, which can largely characterize the original surface.…”

Section: Experimentationmentioning

confidence: 99%

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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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“…The numerical methods certainly provide better insight than experimental methods, but huge computational time is also required (Argatov, 2019). The situation becomes even more complex when surface topography is considered to simulate the real tribological behavior of heavily loaded contacts (Wang et al , 2022). It has been observed that surface topography significantly affects the wear mechanism, friction coefficient and fatigue life of non-conformal contacts (Hansen et al , 2019; Prajapati and Tiwari, 2019; Yan et al , 2015).…”

Section: Introductionmentioning

confidence: 99%

Machine learning approach for the prediction of mixed lubrication parameters for different surface topographies of non-conformal rough contacts

Prajapati,

Katiyar,

Prakash

2023

ILT

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Purpose This study aims to use a machine learning (ML) model for the prediction of traction coefficient and asperity load ratio for different surface topographies of non-conformal rough contacts. Design/methodology/approach The input data set for the ML model is generated using a mixed-lubrication model. Surface topography parameters (skewness, kurtosis and pattern ratio), rolling speed and hardness are used as input features in the multi-layer perceptron (MLP) model. The hyperparameter tuning and fivefold cross-validation are also performed to minimize the overfitting. Findings From the results, it is shown that the MLP model shows excellent accuracy (R2 > 90%) on the test data set for making the prediction of mixed lubrication parameters. It is also observed that engineered rough surfaces with high negative skewness, low kurtosis and isotropic surface patterns exhibit a significant low traction coefficient. It is also concluded that the MLP model gives better accuracy in comparison to the random forest regression model based on the training and testing data sets. Originality/value Mixed lubrication parameters are predicted by developing a regression-based MLP model. The machine learning model is trained using several topography parameters, which are vital in the mixed-EHL regime because of the lack of regression-fit expressions in previous works. The accuracy of MLP with random forest models is also compared.

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A comparative study for selecting and using simulation methods of Gaussian random surfaces

Cited by 14 publications

References 39 publications

Literature review on engineering surface modeling

Literature review on engineering surface modeling

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

Machine learning approach for the prediction of mixed lubrication parameters for different surface topographies of non-conformal rough contacts

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