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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Due to their low vapor pressures, nonflammability, high thermal stabilities, and excellent tribological properties ionic liquids (ILs) are highly attractive lubricant base oils and additives. However, for practical applications of ILs in lubrication, two requirements are often limiting, the required miscibility with standard mineral oils (≥5 wt %) and the complete absence of corrosive halide ions in the ionic liquid. Moreover, the need for full compatibility with standard oil additives reduces the number of potential IL-based lubricant additives even further. In this contribution, an economic halide-free synthesis route to oil-miscible ionic liquids is presented, and very promising tribological properties of such ILs as base oil or additive are demonstrated. Therefore, sliding tests on bearing steel and XPS analysis of the formed surface films are shown. Corrosion test results of different bearing metals in contact with our halide-free ILs and (salt) water prove their applicability as real life lubricants. In the sustainable chemistry and engineering context, we present a halide-free design approach for ionic performance chemicals that may contribute to significant energy savings due to their enhanced lubrication properties.

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