For a tribological experiment involving a steel shaft sliding in a self-lubricating bronze bearing, a semi-supervised machine learning method for the classification of the state of operation is proposed. During the translatory oscillating motion, the system may undergo different states of operation from normal to critical, showing self-recovering behaviour. A Random Forest classifier was trained on individual cycles from the lateral force data from four distinct experimental runs in order to distinguish between four states of operation. The labelling of the individual cycles proved to be crucial for a high prediction accuracy of the trained RF classifier. The proposed semi-supervised approach allows choosing within a range between automatically generated labels and full manual labelling by an expert user. The algorithm was at the current state used for ex post classification of the state of operation. Considering the results from the ex post analysis and providing a sufficiently sized training dataset, online classification of the state of operation of a system will be possible. This will allow taking active countermeasures to stabilise the system or to terminate the experiment before major damage occurs.

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This study deals with a comparison between new experiments on the frictional behavior of porous journal bearings and its prediction by previous numerical simulations. The tests were carried out on bearings lubricated with polyalphaolefin (PAO)-based oils of distinct viscosities. The theoretical model underlying the simulations includes the effects of cavitation by vaporization and accounts for the sinter flow by virtue of Darcy's law. The effective eccentricity ratio corresponding to the experimentally imposed load is estimated by an accurate numerical interpolation scheme. The comparison focuses on the hydrodynamic branches of the Stribeck curve by dimensional analysis (DA), where the variations of the lubricant viscosity with temperature are of main interest. The numerically calculated values of the coefficient of friction are found to reproduce the experimentally obtained ones satisfactorily well in terms of overall trends; yet, the former lie predominantly below the measured ones, which results in a low-positive correlation between the two.

The containers used for steel slag transportation to the recycling depot undergo hightemperature gradients and often deform plastically. Also, parts of the thereby solidified slag adhere to the pot walls, causing demolding problems and wear. A thorough finiteelement analysis of the heat transfer, initiated by filling the pots and essentially driven by radiation, and the thermal stresses is performed. Due to periodic fill-in and discharge, these are assumed to admit a quasi-stationary state referring to the pot temperature before their emptying. The so obtained results aid optimizing the pot shape and the transport process in terms of minimizing maintenance and anti-adhesive cladding. A layer of solidified slag is found to exist throughout the transport process. Although the phase change occurs almost instantaneously at a liquid-solid interface, the "mushy zone" is considered in the accompanying analytical study of the associated Stefan-type problem. Good correlations of the predicted temperatures to the measured ones and the resultant surface damaging are obtained.

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