Optimising the design of textured surfaces for reducing lubricated friction coefficient

Otero, Javier Echávarri; Ochoa, Eduardo de la Guerra; Vallinot, Irene Bellón; Tanarro, Enrique Chacón

doi:10.1002/ls.1363

Cited by 25 publications

(23 citation statements)

References 28 publications

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“…The microgroove depth is an important factor that affects the lubrication performance of the journal bearing and the thrust bearing. 11–28 As shown in Figure 11(a) and (b), the load capacity reaches a maximum and the friction coefficient reaches a minimum at the groove depth of 2.0 μm and 2.5 μm for the eccentricity ratio of 0.2 and 0.4, respectively. Figure 11(a) and (b) also reveals that the load capacity continuously decreases with the increase in microgroove depth when the coupled effect is absent.…”

Section: Resultsmentioning

confidence: 89%

“…It has been demonstrated by numerical simulations and experiments that the textured/microgroove surface can facilitate in improving the lubrication performances for the conformal contact case. 11–24 In addition, the bottom shape of the microgroove plays a crucial role in affecting the hydrodynamic performance. 25–27 Recently, studies have been performed to further understand the influence mechanism of the microgroove with different bottom shapes.…”

Section: Introductionmentioning

confidence: 99%

“…Otero et al. 21 optimized the textured surface according to an experimental investigation in terms of reducing the friction coefficient. The authors found that the maximum friction coefficient reduction can be achieved under the mixed lubrication condition.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A hydrodynamic lubrication model and comparative analysis for coupled microgroove journal-thrust bearings lubricated with water

Xiang

Han

Chen

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part J:

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The novelty of this study is to develop a hydrodynamic lubrication numerical model for coupled microgroove journal-thrust bearings (or coupled bearings) under water-lubricated condition. In the present model, the continuity of the hydrodynamic pressure and the fluid field (or coupled hydrodynamic effect) at common boundary is considered to reveal the mutual effect between the hydrodynamic behavior of the journal bearing and the thrust bearing. The lubrication performances of the coupled microgroove bearing with three bottom shapes, i.e., isosceles triangle, right triangle, and left triangle, are studied comparatively. Additionally, the effects of the microgroove depth on the lubrication performances of the coupled bearing are discussed. The present study reveals that the coupled hydrodynamic effect generated by the coupled bearing can improve the lubrication performance for both the journal and the thrust bearing. The microgroove with left triangle bottom shape yields the optimal lubrication performance as compared to the other two. There is an optimal groove depth that generates the maximum load capacity and the minimum friction coefficient for both the journal and the thrust bearing.

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Section: Resultsmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A hydrodynamic lubrication model and comparative analysis for coupled microgroove journal-thrust bearings lubricated with water

Xiang

Han

Chen

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part J:

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show abstract

“…Otero et al [112] attempted to optimize surface micro-textures fabricated by photolithography and chemical etching processes in order to reduce the COF of EHL contacts by means of an ANN. The data was obtained from tests on a mini-traction machine (steel ball-on-micro-textured copper disk) at various loads, total speeds and slip conditions.…”

Section: Surface Texturingmentioning

confidence: 99%

Current Trends and Applications of Machine Learning in Tribology—A Review

2021

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Machine learning (ML) and artificial intelligence (AI) are rising stars in many scientific disciplines and industries, and high hopes are being pinned upon them. Likewise, ML and AI approaches have also found their way into tribology, where they can support sorting through the complexity of patterns and identifying trends within the multiple interacting features and processes. Published research extends across many fields of tribology from composite materials and drive technology to manufacturing, surface engineering, and lubricants. Accordingly, the intended usages and numerical algorithms are manifold, ranging from artificial neural networks (ANN), decision trees over random forest and rule-based learners to support vector machines. Therefore, this review is aimed to introduce and discuss the current trends and applications of ML and AI in tribology. Thus, researchers and R&D engineers shall be inspired and supported in the identification and selection of suitable and promising ML approaches and strategies.

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“…Recent publications [1,[4][5][6][7][8][9][10][11][12][13] have shown that the pressure-generating effect of textured surfaces that operate in full film lubrication conditions may be the result of three different mechanisms: micro-cavitation [5][6][7][8]12,13] that happens when the pressure in a fluid suddenly drops due to an enlargement of the flow section, convective inertia [1,9] that consists of the inertial force due to the convective acceleration in a flow, and piezo viscosity [10,11], a pressure dependence of viscosity. However, from all these mechanisms that happen in textured surfaces, it seems that micro-cavitation can make a decisive contribution in order to understand the load-carrying capacity effect.…”

Section: Modelling Textured Surfacesmentioning

confidence: 99%

CFD Modeling of the Effect of Different Surface Texturing Geometries on the Frictional Behavior

et al. 2018

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In order to understand the effect of surface texturing parameters on the frictional behavior of textured surfaces and to correlate results of different lubrication regimes, Computational Fluid Dynamics (CFD) numerical analysis of the fluid flow was performed for four different textured surface geometries. The aim of the present research paper is to get theoretical background for the frictional behavior of textured surfaces under hydrodynamic lubrication. Since it is unrealistic to make a direct analysis of a real problem that can possess more than several thousand micro-dimples, the purpose is then to investigate the flow in single cells of periodical micro-dimple patterns and to extract useful conclusions for the lubrication's framework. Among all geometries studied, optimum geometry shapes in terms of hydrodynamic performance were reported. It was found that the best hydrodynamic performance was achieved with the rectangular geometry (lowest shear force).

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Optimising the design of textured surfaces for reducing lubricated friction coefficient

Cited by 25 publications

References 28 publications

A hydrodynamic lubrication model and comparative analysis for coupled microgroove journal-thrust bearings lubricated with water

A hydrodynamic lubrication model and comparative analysis for coupled microgroove journal-thrust bearings lubricated with water

Current Trends and Applications of Machine Learning in Tribology—A Review

CFD Modeling of the Effect of Different Surface Texturing Geometries on the Frictional Behavior

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