Artificial neural network approach to predict the lubricated friction coefficient

Otero, Javier Echávarri; Ochoa, Eduardo de la Guerra; Tanarro, Enrique Chacón; Morgado, Pilar Lafont; Lantada, Andrés Díaz; Muñoz-Guijosa, Juan Manuel; Sanz, José Luis Muñoz

doi:10.1002/ls.1238

Cited by 26 publications

(16 citation statements)

References 30 publications

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“…Artificial neural networks (ANNs) are simplifications of biological neural networks which try, like the latter, to learn from input data to provide the right output . They enable highly complex or non‐linear problems to be solved in a relatively easy manner without having to model the physical phenomena involved …”

Section: Optimising the Designs By Means Of Artificial Neural Networkmentioning

confidence: 99%

Optimising the design of textured surfaces for reducing lubricated friction coefficient

Otero

Ochoa

Vallinot

et al. 2016

Lubrication Science

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Under operating conditions which are unfavourable for lubrication, such as high load and low velocity, the use of textured surfaces significantly promotes the formation of a thick lubricant film and an improvement of the friction coefficient. This paper relates to the manufacture of textures using a photolithography and chemical etching process. Different surface geometries, texturing densities and depths were designed to analyse the influence of these parameters. The friction coefficient was measured in a ball-on-disc tribometer under different lubrication regimes, and the results have been used to develop an artificial neural network with texturing optimisation potential.

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Section: Optimising the Designs By Means Of Artificial Neural Networkmentioning

confidence: 99%

Optimising the design of textured surfaces for reducing lubricated friction coefficient

Otero

Ochoa

Vallinot

et al. 2016

Lubrication Science

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“…The optimization strategies adopted for the calculations were non-sorted genetic algorithm and artificial bee colony algorithm. Otero et al [31] investigated the use of ANNs for predicting the friction coefficient in EHL point contacts. The model was fed with friction data obtained from tribological tests carried out for different lubricants and a range Senatore and Ciortan [28] trained an ANN with excellent prediction quality to optimize the frictional performance of the piston-liner using data obtained from numerical HL simulations.…”

Section: Lubrication and Fluid Film Formationmentioning

confidence: 99%

The Use of Artificial Intelligence in Tribology—A Perspective

et al. 2020

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Artificial intelligence and, in particular, machine learning methods have gained notable attention in the tribological community due to their ability to predict tribologically relevant parameters such as, for instance, the coefficient of friction or the oil film thickness. This perspective aims at highlighting some of the recent advances achieved by implementing artificial intelligence, specifically artificial neutral networks, towards tribological research. The presentation and discussion of successful case studies using these approaches in a tribological context clearly demonstrates their ability to accurately and efficiently predict these tribological characteristics. Regarding future research directions and trends, we emphasis on the extended use of artificial intelligence and machine learning concepts in the field of tribology including the characterization of the resulting surface topography and the design of lubricated systems.

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“…In addition, modern machine learning (ML) or artificial intelligence (AI) algorithms, such as artificial neural networks (ANN), could play a decisive role in an efficient and accurate way, see Table 3. Properly trained models can have an excellent predictive power with a high level of correlation between calculations and simulation or experimental data [94]. Thereby, ML algorithms could be interpreted as a kind of black-box, which determines relevant EHL result variables depending on the input parameters (see Figure 1).…”

Section: Surface Roughness and Asperity Contactmentioning

confidence: 99%

Non-Dimensional Groups, Film Thickness Equations and Correction Factors for Elastohydrodynamic Lubrication: A Review

et al. 2020

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Apart from complex numerical models to predict the tribological behavior of elastohydrodynamically lubricated contacts, non-dimensional similarity groups and analytically solvable proximity equations can be used to estimate integral fluid film parameters. Based upon the pioneering work presented by Dowson and Higginson as well as Blok and Moes, these approaches have been continuously improved over the years by modifications or correction factors to capture different contact geometries (line-, point- or elliptical contacts) as well as to include fluid compression, thermal, non-Newtonian, starvation or roughness effects. Consequently, this review article aims at systematically reviewing these modifications/corrections and discussing their applicability as well as limitations before presenting some recommendations for future research activities.

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Artificial neural network approach to predict the lubricated friction coefficient

Cited by 26 publications

References 30 publications

Optimising the design of textured surfaces for reducing lubricated friction coefficient

Optimising the design of textured surfaces for reducing lubricated friction coefficient

The Use of Artificial Intelligence in Tribology—A Perspective

Non-Dimensional Groups, Film Thickness Equations and Correction Factors for Elastohydrodynamic Lubrication: A Review

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