Today's worries and doubts related to the use of mineral oils increased because of the worldwide interest in environmental issues. This issue has increased the use of vegetable oils as an alternative lubricating oil candidate, environment-friendly lubricant and their additives. In this study, rapeseed oil (RSO) in different concentrations, 1, 2, 3, 5, 10, 20, 30, 40, 50 (by volume percent), was added to base oil to obtain a lubricating oil candidate. Turkish originated RSO was studied as an additive candidate in this paper. The study of the effect of additives in mineral oils was carried out using a specially designed experimental system to compare lubricating oil candidates and high temperatures using engine journal bearings under statically loaded.

Purpose -The purpose of this paper is to study the effects of additive on the tribological properties such as friction coefficient and wear loss in the journal bearings during start-up (or shut-down) and running-in periods. Design/methodology/approach -In this study, fully formulated commercial engine mineral oil (SAE 20W50) and commercial oil additives (3 per cent) added into this oil were tested to determine the tribological performances such as friction coefficient, wear loss and the effect of the additive on protective layers formed on the sliding bearing's surfaces. Findings -This study presents an experimental procedure for obtaining practical results pertaining to the tribological performance of the journal bearing under running-in and start-up or shut down stages. Also, in this study, the authors have attempted to show the linkage between the oil additive and the running conditions such as running-in and start-up and shut-down in effecting performance of the journal bearing. It is well known that one of the roles of an additive is to form a protective layer to reduce friction coefficient in lubricated contacts. Originality/value -The paper is of value in presenting an experimental procedure for obtaining practical results pertaining to the tribological performance of the journal bearing under running-in and start-up or shut down stages.

PurposeThe purpose of this paper is to investigate the effect of a lubricant with a polytetrafluoroethylene (PTFE)‐based additive on the friction behaviour in a steadily loaded journal bearing using an experimental and artificial neural network approach.Design/methodology/approachThe collected experimental data, such as pressure variations, are employed as training and testing data for artificial neural networks (ANNs). A feed forward back propagation algorithm is used to update the weight of the network during the training.FindingsAn artificial neural network predictor has superior performance for modelling journal bearing systems under different lubricant conditions.Research limitations/implicationsA feed forward back propagation algorithm is used as a training algorithm for the proposed neural networks. Various training algorithms can be used to train the proposed network. Various lubricants and concentration ratio of the different additives can be investigated.Practical implicationsThe simulation results suggest that the artificial neural predictor would be used as a predictor for possible experimental applications, especially different lubrication conditions on the modelling journal bearing system.Originality/valueThe paper discusses a new modelling scheme known as ANNs. A neural network predictor has been employed to analyze the effects of a lubricant with a PTFE‐based additive on the friction behaviour in a steadily loaded journal bearing under different operating conditions.

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