Analysis of effects of oil additive into friction coefficient variations on journal bearing using artificial neural network

Durak, Ertuğrul; Salman, Özlem; Kurbanoğlu, Cahit

doi:10.1108/00368790810902241

Cited by 22 publications

(15 citation statements)

References 8 publications

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“…For examples, Jones [9] pointed out the general method for forecasting the friction coefficient based on the ANN. Genal [10], Wu [11] and Durak [12] predicted the influences of fibre contents, load and additive sorts on the friction coefficient of different materials by the ANN model they had established, respectively. What is more, there were also many studies on the forecasting of the wear rate.…”

Section: Introductionmentioning

confidence: 99%

Intelligent tribological forecasting model and system for disc brake

Bao

Tong

Zhu

et al. 2012

2012 24th Chinese Control and Decision Conference (CCDC)

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This paper aims at improving the braking ability and reliability of disc brakes. Based on some braking tests of the disc brake, an intelligent forecasting model for its tribological properties was established firstly by the artificial neural network (ANN) technology. Its input layer contains three braking cells: braking pressure, sliding velocity and surface temperature. And its output layer contains three tribological cells: friction coefficient and its stability coefficient, and wear rate. Secondly, an intelligent forecasting system was developed based on the model. It is mainly composed of three parts: sensing system, data collecting system, and data computing system. Finally, the disc brake used in mine hoists was tested on the system as an example. It is shown that the experimental data which contain nonlinear relationships between the braking conditions and tribological properties of disc brake are theoretical foundations of the tribological forecasting. The ANN is especially suitable for establishing the tribological forecasting model. And the optimized BP network is proved as a simple and effective computing model. The intelligent model and system established in this paper has quite favorable forecasting ability and practicability. By the contrast, it has higher precision for forecasting of the friction coefficient and its stability coefficient than the wear rate. The difference was attributed to man-made testing errors of data samples during tribological experiments.

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Section: Introductionmentioning

confidence: 99%

Intelligent tribological forecasting model and system for disc brake

Bao

Tong

Zhu

et al. 2012

2012 24th Chinese Control and Decision Conference (CCDC)

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“…2) similar to those developed to determine the friction coefficients of lubricated bearings (Durak et al 2008). It has one input layer, one output layer, and two hidden layers.…”

Section: Artificial Neural Network Modelsmentioning

confidence: 97%

Artificial neural network model for estimating the soil temperature

2011

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Ozturk, M., Salman, O. and Koc, M. 2011. Artificial neural network model for estimating the soil temperature. Can. J. Soil Sci. 91: 551Á562. Although soil temperature is a critically important agricultural and environmental factor, it is typically monitored with low spatial resolution and, as a result, methods are required to estimate soil temperature at locations remote from monitoring stations. In this study, cost-effective, feed-forward artificial neural network (ANN) models are developed and tested for estimating soil temperature at 5-, 10-, 20-, 50-and 100-cm depths using standard geographical and meteorological data (i.e., altitude, latitude, longitude, month, year, monthly solar radiation, monthly sunshine duration and monthly mean air temperature). These data plus measured monthly mean soil temperature were collected for 2006Á 2008 from 66 monitoring stations distributed throughout Turkey to obtain a total of 2376 data records (36 months)66 monitoring stations) for each of the five soil depths. At each soil depth, 1800 randomly selected data records were used to develop and train a separate ANN model, and the remaining 576 records at each depth were used to test and validate the resulting models. Good agreement was obtained between ANN-estimated soil temperature and measured soil temperature, as evidenced by correlation coefficients of 98.91, 97.99, 99.03, 98.26 and 95.37% for the 5-, 10-, 20-, 50-and 100-cm soil depths, respectively. It was concluded that ANN modeling is a reliable method for predicting monthly mean soil temperature in regions of Turkey where soil temperature monitoring stations are not present.Ozturk, M., Salman, O. et Koc, M. 2011. Un re´seau neuronal artificiel pour estimer la tempe´rature du sol. Can. J. Soil Sci. 91: 551Á562. Malgre´son importance cruciale en tant que facteur agronomique et environnemental, on surveille habituellement la tempe´rature du sol a`une faible re´solution spatiale. Par conse´quent, on a besoin de me´thodes pour estimer la tempe´rature aux endroits e´loigne´s des stations de surveillance. Dans le cadre de cette e´tude, les auteurs ont mis au point puis teste´des mode`les rentables et sans re´troaction fonde´s sur un re´seau neuronal artificiel (RNA) pour estimer la tempe´rature du sol a`5, 10, 20, 50 et 100 cm de profondeur, a`partir des donne´es ge´ographiques et me´te´orologiques usuelles (par ex., altitude, latitude, longitude, mois, anne´e, rayonnement solaire mensuel, dure´e mensuelle de l'ensoleillement et tempe´rature moyenne de l'air mensuelle). Ces donne´es ont e´te´recueillies de 2006 a`2008 avec la tempe´rature moyenne mensuelle du sol dans 66 stations re´parties un peu partout en Turquie, de manie`re a`obtenir 2 376 releve´s (36 mois ) 66 stations de surveillance) pour chacune des cinq profondeurs. À chaque profondeur, on a utilise´800 releve´s, se´lectionne´s au hasard, pour former un mode`le RNA distinct, les 576 autres releve´s servant a`ve´rifier et a`valider les re´sultats. La tempe´rature du sol estime´e graˆce au RNA concorde bien avec ce...

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“…Lubricants ML/AI approaches have also been used in the development and formulation of lubricants [118] and their additives [119] intended for the use in tribological systems. As such, Durak et al [120] analyzed the effects of PTFE-based additives in mineral oil onto the frictional behavior of hydrodynamic journal bearings (252 data sets) by the aid of a feed forward back propagation ANN. An architecture with three inputs as studied in respective experiments (load, velocity, additive concentration), two hidden layers of 5 and 3 neurons, and the COF as output resulted in an accuracy of 98%.…”

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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Analysis of effects of oil additive into friction coefficient variations on journal bearing using artificial neural network

Cited by 22 publications

References 8 publications

Intelligent tribological forecasting model and system for disc brake

Intelligent tribological forecasting model and system for disc brake

Artificial neural network model for estimating the soil temperature

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

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